WO2010089987A1 - 再生信号評価方法、再生信号評価装置及びこれを備えた光ディスク装置 - Google Patents
再生信号評価方法、再生信号評価装置及びこれを備えた光ディスク装置 Download PDFInfo
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- WO2010089987A1 WO2010089987A1 PCT/JP2010/000589 JP2010000589W WO2010089987A1 WO 2010089987 A1 WO2010089987 A1 WO 2010089987A1 JP 2010000589 W JP2010000589 W JP 2010000589W WO 2010089987 A1 WO2010089987 A1 WO 2010089987A1
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B20/00—Signal processing not specific to the method of recording or reproducing; Circuits therefor
- G11B20/10—Digital recording or reproducing
- G11B20/10009—Improvement or modification of read or write signals
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B20/00—Signal processing not specific to the method of recording or reproducing; Circuits therefor
- G11B20/10—Digital recording or reproducing
- G11B20/10009—Improvement or modification of read or write signals
- G11B20/10046—Improvement or modification of read or write signals filtering or equalising, e.g. setting the tap weights of an FIR filter
- G11B20/10055—Improvement or modification of read or write signals filtering or equalising, e.g. setting the tap weights of an FIR filter using partial response filtering when writing the signal to the medium or reading it therefrom
- G11B20/1012—Improvement or modification of read or write signals filtering or equalising, e.g. setting the tap weights of an FIR filter using partial response filtering when writing the signal to the medium or reading it therefrom partial response PR(1,2,2,2,1)
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B20/00—Signal processing not specific to the method of recording or reproducing; Circuits therefor
- G11B20/10—Digital recording or reproducing
- G11B20/10009—Improvement or modification of read or write signals
- G11B20/10481—Improvement or modification of read or write signals optimisation methods
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B20/00—Signal processing not specific to the method of recording or reproducing; Circuits therefor
- G11B20/10—Digital recording or reproducing
- G11B20/18—Error detection or correction; Testing, e.g. of drop-outs
- G11B20/1816—Testing
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B2220/00—Record carriers by type
- G11B2220/20—Disc-shaped record carriers
- G11B2220/25—Disc-shaped record carriers characterised in that the disc is based on a specific recording technology
- G11B2220/2537—Optical discs
Definitions
- the present invention relates to a reproduction signal evaluation method using a PRML signal processing method, a reproduction signal evaluation apparatus, and an optical disk apparatus including the same.
- the PRML system is a technique that combines partial response (PR) and maximum likelihood decoding (ML), and is a known system that selects the most probable signal sequence from a reproduced waveform on the assumption that intersymbol interference occurs. For this reason, it is known that decoding performance improves compared with the conventional level determination system (for example, refer nonpatent literature 1).
- the system margin can be maintained by adopting a higher-order PRML method (see, for example, Non-Patent Document 1).
- a higher-order PRML method see, for example, Non-Patent Document 1
- the system margin can be maintained by adopting the PR1221ML system, but the recording per recording layer is possible.
- the capacity is 33.3 GB, it is necessary to adopt the PR12221ML system.
- Patent Documents 1 and 2 disclose that “a difference metric that is a difference between a reproduction signal of a most probable first state transition sequence and a second most probable second state transition sequence” is used as an index value. Has been.
- Patent Document 1 if there are multiple patterns of “the most probable first state transition sequence and the second most probable second state transition sequence” that may cause an error, it is necessary to comprehensively process these patterns. There is.
- This processing method is not disclosed in Patent Document 1 and Patent Document 2.
- Patent document 5 focuses on this point and detects “the most probable first state transition sequence and the second most probable second state transition sequence” detected by the same method as in patent document 1 and patent document 2.
- a method of detecting a plurality of “difference metrics with respect to a reproduction signal” and processing the pattern group is disclosed.
- Patent Document 5 as an error prediction method, it is assumed that the distribution of the obtained index value is a normal distribution, and the probability that the index value is 0 or less from the standard deviation ⁇ and the variance average value ⁇ , that is, a bit error is caused. A method of predicting probability is used. However, this is a general method for predicting the error occurrence probability.
- the prediction error rate calculation method of Patent Document 5 is characterized in that an occurrence probability is obtained for each pattern, a prediction error rate is calculated, and this prediction error rate is used as a measure of signal quality.
- Patent Document 5 has a problem that the error rate cannot be accurately predicted when recording distortion occurs in the recording signal. This problem is particularly noticeable when data is recorded by thermal recording such as an optical disk because recording distortion due to thermal interference is likely to occur. Further, since the interval between the recording pits becomes narrower as the density of the optical disk becomes higher, it is predicted that the thermal interference will increase, and this problem will become an unavoidable problem in the future. The problem that the prediction error rate calculation method described in Patent Document 5 cannot appropriately evaluate the signal quality for a signal with recording distortion will be specifically described below.
- FIG. 21 shows an example of the frequency distribution of the differential metric of a specific pattern used as a signal index in Patent Document 1 and Patent Document 5.
- the spread of the difference metric distribution is caused by noise generated in the optical disc. Since the reproduction noise generated in the optical disk is random noise, this distribution is usually such a normal distribution.
- This difference metric is defined as “the difference metric between the most probable first state transition sequence and the second most probable second state transition sequence”, and the most probable first state transition sequence of the ideal signal.
- the distribution is centered on the square of the Euclidean distance (hereinafter defined as a signal processing threshold) between the second state transition sequence and the second most likely state transition sequence.
- the standard deviation centered on this signal processing threshold is an index value defined in Patent Documents 1, 2, and 5.
- the probability that this difference metric becomes 0 or less corresponds to the prediction error rate.
- This prediction error rate can be obtained from an inverse function of the cumulative distribution function of the normal distribution.
- FIG. 21A is a distribution diagram in the case where almost no distortion occurs during recording.
- FIGS. 21B and 21C are distribution diagrams in which the recording edge of the recording pit shifts due to thermal interference during recording and recording distortion occurs. Is shown.
- the frequency distribution of the differential metric of a specific pattern becomes a normal distribution with a shifted center value. This shift of the center position corresponds to the distortion generated by thermal interference.
- FIG. 21B and FIG. 21C are cases in which a certain amount of shift occurs positively and negatively from the center of the distribution.
- the index values obtained are the same value, and the index value increases when the center of the distribution is shifted. An increase in the index value means that the probability that an error will occur has increased, but in FIG. 21C, the problem that the error decreases conversely occurs.
- FIG. 21D shows a case where the obtained difference metric distribution is not a normal distribution.
- the thermal interference during recording is large and there is thermal interference from recording marks before and after the “most probable first state transition sequence and the second most probable second state transition sequence”. appear.
- the amount of thermal interference differs depending on the length of the recording mark before and after, and the recording mark position is shifted, resulting in a differential metric distribution in which two normal distributions (distribution 1 and distribution 2) overlap.
- Distribution 2 is shifted to the plus side of the signal processing threshold value, so the probability of causing an error is reduced, but the index value, which is a standard deviation centered on the signal processing threshold value, increases due to the influence of distribution 2. To do. In this case, similarly to FIG. 21C, there arises a problem that the error rate decreases even if the index value increases. As described above, when the conventional techniques such as Patent Document 1 and Patent Document 5 are applied to a high-density optical disk recording product with large thermal interference, there is a problem that the correlation between the index value and the error rate is deteriorated. .
- Patent Document 4 This is a method of counting the number in which the difference metric obtained by a predetermined pattern group becomes smaller than a predetermined threshold (for example, half of the signal processing threshold). A method for obtaining a prediction error rate from the count value is also disclosed. In the case of this method, since the side close to 0 of the differential metric distribution, that is, the side that may cause an error is used as an evaluation target, the above-described problems of Patent Document 1 and Patent Document 5 do not occur. However, the following new problem arises due to the configuration in which the number exceeding this value is measured using a specific threshold. This problem will be described with reference to FIG. 21E.
- FIG. 21E shows an example in which the threshold is half of the signal processing threshold and the number of distributions exceeding this value is counted. The value below this threshold is counted to determine its value, and the ratio of the pattern generation parameter to the count value is used as the signal index. Assuming that the distribution of the differential metric is a normal distribution from this count ratio, the probability that the differential metric is smaller than 0 can be obtained, and the prediction error rate can be calculated.
- FIG. 21F shows an example of the frequency distribution in the case where the signal quality is good (signal quality of about 8% jitter). In such a case, the spread of the difference metric distribution is narrowed, and the number exceeding the threshold is extremely reduced.
- Patent Document 4 does not have the merit of being strong against defects that had the conventional time-axis jitter, and is used as an index value for an optical disk in which defects are likely to occur due to scratches or fingerprints. There was a problem to use. In order to increase the number of measurements by the method of Patent Document 4, it is only necessary to increase the number of measurable by increasing the threshold. However, when the threshold is increased, there is another problem that the accuracy of the error rate predicted decreases. appear. In an extreme example, if the threshold is increased to half the Euclidean distance, the number exceeding the threshold is half the number of samples for which the differential metric was measured, so it does not depend on the spread of the distribution and correct measurement is impossible. .
- Patent Literature 4 and Patent Literature 5 disclose a method using bER predicted from a difference metric as an index. However, when these are used as index values, they have been used as signal quality evaluation indexes of conventional optical discs. There is a problem that it is not compatible with jitter on the time axis and is difficult to handle.
- the present invention has been made to solve the above problems, and provides a signal processing method, a reproduction signal evaluation apparatus, and an optical disc apparatus including the signal processing method, which can evaluate the quality of a reproduction signal of an information recording medium with high accuracy. It is intended to provide.
- a reproduction signal evaluation method is a reproduction that evaluates the quality of a reproduction signal based on a binarized signal generated by using a PRML signal processing method from a reproduction signal reproduced from an information recording medium.
- a signal evaluation method for extracting a specific state transition pattern that may cause a bit error from the binarized signal, and the binary value of the state transition pattern extracted in the pattern extraction step Based on the binarized signal, the first metric between the ideal signal of the first state transition sequence most likely corresponding to the binarized signal and the reproduction signal, and the second corresponding to the binarized signal
- a difference metric calculation step for calculating a difference metric that is a difference between an ideal signal of the likely second state transition sequence and a second metric between the reproduced signal;
- a first integration step for integrating the difference metrics calculated in the difference metric calculation step; a first count step for counting the number of integration processes in the first integration step; and the predetermined signal processing threshold value or less
- a differential metric extraction step for extracting a differential metric,
- the present invention depending on the recording state, when the average value of the difference metric does not match the code distance of the ideal signal, the standard deviation generated by the deviation of the average value of the difference metric from the code distance of the ideal signal.
- the correlation between the error rate and the signal index value is improved, and the information recording medium is reproduced. Signal quality can be evaluated with high accuracy.
- FIG. 1 is a block diagram showing a schematic configuration of an optical disc apparatus according to an embodiment of the present invention. It is a block diagram which shows the structure of the optical disk apparatus based on other embodiment of this invention. It is a figure which shows the state transition rule defined from RLL (1,7) recording code and equalization system PR (1,2,2,2,1) concerning one embodiment of this invention.
- FIG. 4 is a trellis diagram corresponding to the state transition rule shown in FIG. 3. It is a figure which shows the relationship between the sample time in the transition path of Table 1, and a reproduction level (signal level). It is a figure which shows the relationship between the sample time in the transition path of Table 2, and a reproduction level (signal level).
- FIG. 16A is a distribution diagram showing the range of the differential metric in the third and fourth embodiments
- FIG. 16B is a distribution diagram showing the range of the differential metric in the fifth embodiment.
- FIG. 17A and FIG. 17B are diagrams for explaining a standard deviation calculation method in the fifth embodiment.
- the average value of the difference metric and a variable b 1 (b x), is a diagram showing the relationship between the variables a 1 (a x) and standard deviation ⁇ 1/2 ( ⁇ x / 2). It is a block diagram which shows the structure of the optical disk apparatus based on further another embodiment of this invention.
- 20A and 20B are diagrams for explaining a standard deviation calculation method according to the sixth embodiment.
- FIG. 21A is an explanatory diagram showing a distribution map of a conventional differential metric.
- FIG. 21B is an explanatory diagram showing a distribution diagram of a conventional differential metric.
- FIG. 21C is an explanatory diagram showing a distribution diagram of a conventional differential metric.
- FIG. 21D is an explanatory diagram illustrating a distribution diagram of a conventional differential metric.
- FIG. 21E is an explanatory diagram showing a distribution map of a conventional differential metric.
- FIG. 21F is an explanatory diagram illustrating a distribution diagram of a conventional differential metric.
- the signal evaluation index detection apparatus employs a PR12221ML system, which is an example of a PRML system, for reproduction-system signal processing, and uses an RLL (Run Length Limited) code such as an RLL (1, 7) code as a recording code. Used.
- RLL Un Length Limited
- the waveform equalization technique that corrects the reproduction distortion that occurs when information is reproduced and the redundancy of the equalization waveform itself are actively used, and the most reliable method is based on the reproduced signal containing data errors. This is signal processing combined with signal processing technology for selecting a new data series.
- FIG. 3 is a state transition diagram showing a state transition rule determined from the RLL (1, 7) recording code and the PR12221ML system.
- FIG. 3 shows a state transition diagram generally used when explaining PRML.
- FIG. 4 is a trellis diagram in which the state transition diagram shown in FIG. 3 is developed with respect to the time axis.
- the number of states of the decoding unit is limited to 10 in combination with RLL (1, 7).
- the number of state transition paths in the PR12221ML system is 16, and the playback level is 9 levels.
- state S (0, 0, 0, 0) at a certain time is represented as S0, state S (0, 0, 0, 1).
- S1, state S (0,0,1,1) is S2
- state S (0,1,1,1) is S3
- state S (1,1,1,1) is S4,
- state S (1,1 , 1, 0) is S5, state S (1, 1, 0, 0) is S6,
- state S (1, 0, 0, 0) is S7
- state S (1,0, 0, 1) is S8,
- State S (0, 1, 1, 0) is expressed as S9 and 10 states are expressed.
- “0” or “1” described in parentheses indicates a signal sequence on the time axis, and indicates which state may be brought about by a state transition from a certain state to the next time. Show.
- Each table in Tables 1 to 3 shows the state transition indicating the trajectory of the state merged from the start state, two transition data strings that may have passed through the state transition, and the possibility through the state transition 2 shows two ideal reproduced waveforms having characteristics and square values of Euclidean distances of the two ideal reproduced waveforms.
- the square value of the Euclidean distance indicates the square addition of the difference between two ideal reproduction waveforms.
- the value of the Euclidean distance is large, it becomes easier to distinguish, and therefore the possibility of erroneous determination is reduced.
- the value of the Euclidean distance is small, it is difficult to distinguish two possible waveforms, so that the possibility of erroneous determination is increased. That is, a state transition pattern having a large Euclidean distance is a state transition pattern in which an error is unlikely to occur, and a state transition pattern having a small Euclidean distance is a state transition pattern in which an error is likely to occur.
- the first column indicates a state transition (Sm k-9 ⁇ Sn k ) in which two state transitions that are likely to cause an error branch and rejoin.
- the second column shows a transition data string (b k ⁇ i ,..., B k ) that generates this state transition.
- X in this transition data string indicates a bit that is likely to cause an error in these data.
- the number of X (Tables 2 and 3). No.! X is also the number of errors. That is, X in the transition data string can be 1 or 0. Either 1 or 0 corresponds to the most probable first state transition sequence, and the other corresponds to the second most probable second state transition sequence. In Table 2 and Table 3,! X represents the bit inversion of X.
- each decoded data sequence (binarized signal) subjected to the decoding process by the Viterbi decoding unit is compared with the transition data sequence in Tables 1 to 3 (X is don't care), The most probable first state transition sequence that is likely to cause an error and the second most probable second state transition sequence are extracted.
- the third column shows the first state transition sequence and the second state transition sequence.
- the fourth column shows two ideal reproduction waveforms (PR equivalent ideal values) when passing through the respective state transitions, and the fifth column is the square of the Euclidean distance of the two ideal signals. Value (square value of Euclidean distance between paths).
- Table 1 shows a state transition pattern that can take two state transitions, and shows a state transition pattern in which the square value of the Euclidean distance is 14. There are 18 types of state transition sequence patterns when the square value of the Euclidean distance is 14.
- the state transition sequence pattern shown in Table 1 corresponds to the edge (switch between mark and space) of the waveform of the optical disc. In other words, the state transition sequence pattern shown in Table 1 is an edge 1-bit shift error pattern.
- FIG. 5 is a graph showing the relationship between the sample time and the reproduction level (signal level) in the transition path of Table 1.
- the horizontal axis indicates the sample time (sampling at every time of the recording sequence), and the vertical axis indicates the playback level.
- the ideal reproduction signal level is 9 levels (from 0 level to 8 levels).
- One transition path in this case is a case where the recording sequence is detected by transitioning to “0, 0, 0, 0, 1, 1, 1, 0, 0”.
- the recording state is a space having a length of 4T space or more, a 3T mark, and a 2T space or more. It becomes a space of length.
- the relationship between the sample time and the reproduction level (signal level) in the above transition path is shown as an A path waveform in FIG.
- the recording sequence of another transition path from the state S0 (k-5) to the state S6 (k) in the state transition rule shown in FIG. 5 is “0, 0, 0, 0, 0”. , 1, 1, 0, 0 ".
- the PR equivalent ideal waveform of the path is shown as a B path waveform in FIG.
- the state transition pattern in which the square value of the Euclidean distance in Table 1 is 14 is characterized in that it always includes one piece of edge information (zero cross point).
- FIG. 6 is a graph showing the relationship between the sample time and the reproduction level (signal level) in the transition path of Table 2.
- the horizontal axis indicates the sample time (sampling at every time of the recording sequence), and the vertical axis indicates the playback level.
- Table 2 shows a state transition pattern that can take two state transitions similarly to Table 1, and shows a state transition pattern in which the square value of the Euclidean distance is 12. There are 18 types of state transition patterns when the square value of the Euclidean distance is 12.
- the state transition patterns shown in Table 2 are 2T mark or 2T space shift errors, and are 2-bit shift error patterns.
- one path in which the recording sequence transitions to “0, 0, 0, 0, 1, 1, 0, 0, 0, 0” is detected.
- 1 ′′ is replaced with a mark portion, it corresponds to a space having a length of 4T space or more, a 2T mark, or a space having a length of 5T space or more.
- the PR equivalent ideal waveform of the path is shown as an A path waveform in FIG.
- a transition path when transitioning from state S0 (k-7) to state S0 (k) in the state transition rule shown in FIG. 3 will be described (see Table 2).
- One transition path in this case is a case where the recording sequence is detected by transitioning to “0, 0, 0, 0, 1, 1, 0, 0, 0, 0”.
- “0” of the reproduction data is a space portion and “1” is a mark portion and the transition path is placed in a recording state
- the recording state is a space having a length of 4T space or more, a 2T mark, and a 5T space or more. It becomes a space of length.
- FIG. 6 shows the relationship between the sample time and the reproduction level (signal level) in the above transition path as an A path waveform.
- the other transition path is a case where the recording sequence is detected by transitioning to “0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0”.
- “0” of the reproduction data is a space portion and “1” is a mark portion and the transition path is replaced with a recording state
- the recording state is a space having a length of 5T space or more, a 2T mark, and a 4T space or more. It becomes a space of length.
- FIG. 6 shows the relationship between the sample time and the reproduction level (signal level) in the above transition path as a B path waveform.
- the state transition pattern in which the square value of the Euclidean distance in Table 2 is 12 is characterized in that it always includes two pieces of 2T rising and falling edge information.
- FIG. 7 is a graph showing the relationship between the sample time and the reproduction level (signal level) in the transition path of Table 3.
- the horizontal axis indicates the sample time (sampling at every time of the recording sequence), and the vertical axis indicates the playback level.
- Table 3 shows a state transition sequence pattern that can take two state transition sequences as in Tables 1 and 2, and shows a state transition sequence pattern when the square value of the Euclidean distance is 12. There are 18 types of state transition sequence patterns when the square value of the Euclidean distance is 12.
- the state transition sequence pattern shown in Table 3 is a portion where a 2T mark and a 2T space are continuous, and is a 3-bit shift error pattern.
- One transition path in this case is a case where the recording sequence is detected by transitioning to “0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0”.
- the recording state is a space having a length of 4T space or more, 2T mark, 2T space, 3T. It becomes a mark and a space having a length of 2T space or more.
- the other transition path is a case where the recording sequence is detected by transitioning to “0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0”.
- “0” of the reproduction data is a space part and “1” is a mark part and the transition path is replaced with a recording state
- the recording state is a space having a length of 5T space or more, 2T mark, 2T space, 2T It becomes a mark and a space having a length of 2T space or more.
- the relationship between the sample time and the reproduction level (signal level) in the above transition path is shown as a B path waveform in FIG.
- the state transition sequence pattern in which the square value of the Euclidean distance in Table 3 is 12 is characterized in that it is a pattern including at least three pieces of edge information.
- FIG. 1 is a block diagram illustrating a configuration of an optical disc device 200 according to the first embodiment.
- the information recording medium 1 is an information recording medium for optically recording and reproducing information, for example, an optical disk medium.
- the optical disc device 200 is a reproducing device that reproduces information from the mounted information recording medium 1.
- the optical disk device 200 includes an optical head unit 2, a preamplifier unit 3, an AGC (Automatic Gain Controller) unit 4, a waveform equalization unit 5, an A / D conversion unit 6, a PLL (Phase Locked Loop) unit 7, a PR equalization unit 8 A maximum likelihood decoding unit 9, a signal evaluation index detection unit (reproduction signal evaluation device) 100, and an optical disk controller unit 15.
- the optical head unit 2 converges the laser light that has passed through the objective lens on the recording layer of the information recording medium 1, receives the reflected light, and generates an analog reproduction signal indicating information read from the information recording medium 1.
- the preamplifier unit 3 amplifies the analog reproduction signal generated by the optical head unit 2 with a predetermined gain and outputs the amplified signal to the AGC unit 4.
- the numerical aperture of the objective lens is 0.7 to 0.9, more preferably 0.85.
- the wavelength of the laser light is 410 nm or less, more preferably 405 nm.
- the preamplifier unit 3 amplifies the analog reproduction signal with a predetermined gain and outputs the amplified signal to the AGC unit 4.
- the AGC unit 4 amplifies or attenuates the analog reproduction signal based on the output from the A / D conversion unit 6 so that the analog reproduction signal from the preamplifier unit 3 has a predetermined amplitude, and the waveform equalization unit 5 Output to.
- the waveform equalization unit 5 has an LPF characteristic that cuts off the high frequency range of the reproduction signal and an HPF characteristic that cuts off the low frequency range of the reproduction signal. 6 is output.
- the A / D conversion unit 6 samples the analog reproduction signal in synchronization with the reproduction clock output from the PLL unit 7, converts the analog reproduction signal into a digital reproduction signal, outputs the digital reproduction signal to the PR equalization unit 8, and outputs the AGC. Also output to the unit 4 and the PLL unit 7.
- the PLL unit 7 generates a reproduction clock synchronized with the reproduction signal after waveform equalization based on the output from the A / D conversion unit 6, and outputs it to the A / D conversion unit 6.
- the PR equalization unit 8 has a function of changing the filter characteristics to the characteristics of various PR methods.
- the PR equalization unit 8 has a frequency set so that the frequency characteristic of the reproduction system becomes a characteristic assumed by the maximum likelihood decoding unit 9 (for example, PR (1, 2, 2, 2, 1) equalization characteristic, etc.). Filtering is performed so as to obtain characteristics, and PR equalization processing for suppressing high-frequency noise and intentionally adding intersymbol interference is performed on the digital reproduction signal, and the result is output to the maximum likelihood decoding unit 9.
- the PR equalization unit 8 may include, for example, an FIR (Finite Impulse Response) filter configuration, and may adaptively control the tap coefficient using an LMS (The Last-Mean Square) algorithm (non-conversion). Patent Document 2).
- the maximum likelihood decoding unit 9 is, for example, a Viterbi decoder, and uses a maximum likelihood decoding method for estimating a likely sequence based on a code rule intentionally added according to the partial response type.
- the maximum likelihood decoding unit 9 decodes the reproduction signal PR-equalized by the PR equalization unit 8 and outputs binarized data. This binarized data is output as a decoded binarized signal to the optical disk controller unit 15 at the subsequent stage, and a predetermined process is executed to reproduce information recorded on the information recording medium 1.
- the signal evaluation index detection unit 100 includes a pattern detection unit 101, a difference metric calculation unit 102, a magnitude determination unit 103, a pattern count unit 104, an integration unit 105, an error calculation unit 116, and a standard deviation calculation unit 120.
- the signal evaluation index detection unit 100 receives the waveform-shaped digital reproduction signal output from the PR equalization unit 8 and the binarized signal output from the maximum likelihood decoding unit 9. In the signal evaluation index detection unit 100, the binarized signal is input to the pattern detection unit 101, while the digital reproduction signal is input to the difference metric calculation unit 102, and the digital reproduction signal evaluation process of the information recording medium 1 is performed. Will be executed.
- the pattern detection unit 101 has a function of extracting a specific state transition pattern that may cause a bit error from the binarized signal.
- the pattern detection unit 101 according to the present embodiment has a square value of the Euclidean distance between the ideal signal of the most probable first state transition sequence and the ideal signal of the second most probable second state transition sequence. 14 is extracted (that is, the state transition pattern shown in Table 1).
- the pattern detection unit 101 stores information on the state transition patterns shown in Table 1. Then, the pattern detection unit 101 compares the transition data string in Table 1 with the binarized signal output from the maximum likelihood decoding unit 9.
- the binarized signal matches the transition data string in Table 1, the binarized signal is selected as an extraction target, and the most reliable corresponding to the binarized signal is based on the information in Table 1. A likely first state transition sequence and a second most likely second state transition sequence are selected.
- the difference metric calculating unit 102 reads “an ideal signal (PR or the like of the most probable first state transition sequence corresponding to the binarized signal).
- the “difference metric” that is the absolute value of the difference from the “second metric between” is calculated.
- the first metric is the square value of the Euclidean distance between the ideal signal of the first state transition sequence and the digital reproduction signal
- the second metric is the ideal signal and digital of the second state transition sequence. It is the square value of the Euclidean distance between the playback signal.
- the output from the difference metric calculation unit 102 is input to the magnitude determination unit 103 and compared with a predetermined value (signal processing threshold).
- the pattern count unit 104 counts the number of differential metrics that are less than or equal to the signal processing threshold. This count value is the frequency of occurrence of each pattern group when calculating the error rate.
- the accumulating unit 105 accumulates difference metrics that are equal to or less than the signal processing threshold. When the integrated value obtained by the integrating unit 105 is divided by the number of occurrences of the pattern, an average value of difference metrics that are equal to or less than the signal processing threshold value can be obtained.
- the error calculation unit 116 calculates a prediction error rate from each integrated value of the difference metric equal to or less than the signal processing threshold and the number of pattern occurrences. Then, the standard deviation calculation unit 120 calculates a standard deviation corresponding to the error rate, and uses the standard deviation as a signal index value for evaluating signal quality.
- the process by the signal evaluation index detection unit 100 will be described in detail below.
- the reproduction signal reproduced from the information recording medium 1 by the PRML process is output as a binarized signal from the maximum likelihood decoding unit 9 and input to the signal evaluation index detection unit 100 as described above.
- the PR equalization ideal values of the first state transition string and the second state transition string are determined. For example, in Table 1, when (0, 0, 0, 0, X, 1, 1, 0, 0) is decoded as a binarized signal, the most probable first state transition sequence is (S0 , S1, S2, S3, S5, S6) and (S0, S0, S1, S2, S9, S6) are selected as the second most probable second state transition sequence.
- the PR equalization ideal value corresponding to the first state transition sequence is (1, 3, 5, 6, 5).
- the PR equalization ideal value corresponding to the second state transition sequence is (0, 1, 3, 4, 4).
- the difference metric calculation unit 102 has a first metric (Pb) that is a square value of the Euclidean distance between the reproduction signal sequence (digital reproduction signal) and the PR equalization ideal value corresponding to the first state transition sequence. 14 ).
- the differential metric calculation unit 102 obtains a second metric (Pa 14 ) that is a square value of the Euclidean distance between the reproduction signal sequence and the PR equalization ideal value corresponding to the second state transition sequence.
- the area larger than the signal processing threshold is an area that does not cause an error, and is an area that is not necessary for predicting the error rate. Therefore, in order to predict the error rate from the standard deviation of the difference metric, attention should be paid to the area below the signal processing threshold.
- the error rate calculation method will be described below.
- Differential metric D 14 is the output from the differential metric computing section 102 is inputted to the magnitude determination unit 103, it is compared with a predetermined value (signal processing threshold).
- the signal processing threshold corresponding to the specific state transition pattern to be extracted has the most probable ideal signal of the first state transition sequence and the second most probable ideal signal of the second state transition sequence. Is set to “14”, which is the square value of the Euclidean distance between. If the difference metric D 14 is equal to or less than the signal processing threshold “14”, the size determination unit 103 outputs the value of the difference metric D 14 to the integration unit 105, and the pattern count unit 104 counts up the count value.
- the accumulation unit 105 accumulates the difference metric cumulatively every time the difference metric D 14 that is equal to or less than the signal processing threshold is input. Then, the error calculation unit 116 calculates a prediction error rate from the integrated value of the difference metric equal to or less than the signal processing threshold and the number of pattern occurrences counted by the pattern count unit 104. The operation of this error calculation unit 116 will be described below.
- an average value of the differential metrics that are equal to or smaller than the signal processing threshold can be obtained. it can.
- M the average value of the difference metric below the signal processing threshold
- the average value of the distribution function is ⁇
- the standard deviation is ⁇ 14
- the probability density function is f
- the distribution function is assumed to be a normal distribution
- the absolute value average value m of the difference metric less than or equal to the processing threshold is expressed by the following equation (4).
- the difference metric absolute value average value m below the signal processing threshold is obtained and then about 1.253 times. You can see that Since the signal processing threshold is fixed, the standard deviation ⁇ 14 can be calculated from the absolute value average value m. Then, the probability of occurrence of an error (error rate bER 14 ) calculated by the error calculation unit 116 can be obtained from the following equation (6).
- d 14 in the equation (6) is between the ideal signal of the most probable first state transition sequence and the second most probable ideal signal of the second state transition sequence in the state transition pattern to be extracted.
- the standard deviation calculation unit 120 converts the error rate (error occurrence probability) bER 14 into a signal index value M for use as an index that can be handled in the same manner as jitter.
- the standard deviation calculation unit 120 converts bER 14 into a signal index value M using the standard deviation ⁇ corresponding to the predicted error rate by the following equation (7).
- erfc () is an integral value of the complementary error function.
- the definition formula of the signal index value M of the present embodiment is the following formula (8)
- the virtual standard deviation ⁇ is used by substituting the bER 14 calculated by the formula (6) into the formula (7). Index value M can be obtained.
- the virtual standard deviation ⁇ and the signal index value M were calculated from the predicted error rate using the equations (6) to (8).
- the signal evaluation index M is generated from the difference metric information of the state transition sequence pattern. ing. Specifically, the error rate predicted from the average value of the difference metric information below the threshold of signal processing is calculated, the standard deviation ⁇ of the virtual normal distribution is calculated from the error rate, and the standard deviation of the normal distribution is calculated. A signal evaluation index M including ⁇ is generated. As a result, it is possible to provide a signal evaluation method and an evaluation index that are highly correlated with the error rate.
- a signal index having a correlation with the error rate is generated due to recording distortion caused by thermal interference or the like that is increasingly required in the future in an optical disk with higher density. It is difficult to calculate.
- the present embodiment solves this problem, and in order to calculate a signal index highly correlated with an error that actually occurs, focusing only on the one-sided distribution in which an error occurs among the distribution components of the differential metric, The point is to obtain the standard deviation ⁇ of the virtual two-sided distribution from the one-sided distribution.
- the pattern detection unit 101 uses the most probable first state transition sequence ideal signal and the second state transition pattern.
- a specific state transition pattern that is, the state transition pattern shown in Table 1 in which the square value of the Euclidean distance from the ideal signal of the second state transition sequence that is likely to be 14 is extracted is extracted.
- a specific state transition pattern in which the square value of the Euclidean distance is 12 that is, a state transition pattern shown in Table 2 or Table 3 may be extracted.
- the optical disk controller unit 15 functions as an evaluation unit that performs an evaluation process based on the signal evaluation index M received from the standard deviation calculation unit 120.
- the evaluation result can be displayed on a display unit (not shown) or stored in a memory as evaluation data.
- the optical disk apparatus 200 including the signal evaluation index detection unit 100 has been described.
- an optical disk evaluation apparatus (reproduction signal evaluation apparatus) including the optical disk controller unit 15 as an evaluation unit may be used.
- the optical disk evaluation apparatus can be used mainly for the information recording medium 1 before shipment from the factory to evaluate whether or not the information recording medium 1 has a quality conforming to a predetermined standard.
- the optical disc apparatus 200 provided with the reproduction signal evaluation apparatus can be set to perform the following operation.
- the quality of the reproduction signal is evaluated for a commercially available optical disc (blank disc) shipped from the factory, and when it is determined that the predetermined quality is not satisfied, the optical disc is ejected to the outside.
- the evaluation is performed on an optical disc (recording other than the optical disc apparatus) already recorded by the recorder and it is determined that the predetermined quality is not satisfied, the optical disc can be ejected to the outside. is there.
- the optical disc apparatus 200 can record and reproduce information, it can be evaluated by test recording before recording information on the optical disc. In this case, the quality of the reproduced signal is evaluated with respect to the test recording information recorded by the optical disc device 200, and if it is NG, the recording condition is adjusted until it becomes OK. It can be discharged to the outside.
- FIG. 2 is a block diagram showing a configuration of the optical disc apparatus 400 according to the second embodiment.
- the information recording medium 1 is an information recording medium for optically recording and reproducing information, for example, an optical disk medium.
- the optical disc apparatus 400 is a reproducing apparatus that reproduces information from the mounted information recording medium 1.
- the optical disk device 400 includes an optical head unit 2, a preamplifier unit 3, an AGC (Automatic Gain Controller) unit 4, a waveform equalization unit 5, an A / D conversion unit 6, a PLL (Phase Locked Loop) unit 7, and a PR equalization unit 8.
- the signal evaluation index detection unit 300 is an evaluation for determining whether or not the information recording medium 1 has a quality conforming to a predetermined standard before shipment. It can be used as a device. Further, the signal evaluation index detection unit 300 can be mounted on a drive device of the information recording medium 1 and can be used as an evaluation device for performing test recording before the user records information on the information recording medium 1. .
- the signal evaluation index detection unit 300 includes pattern detection units 101, 106, 111, difference metric calculation units 102, 107, 112, size determination units 103, 108, 113, pattern count units 104, 109, 114, and integration units 105, 110. , 115, error calculation units 116, 117, 118, an addition unit 119, and a standard deviation calculation unit 120.
- the signal evaluation index detection unit 300 receives the waveform-shaped digital reproduction signal output from the PR equalization unit 8 and the binarized signal output from the maximum likelihood decoding unit 9.
- the pattern detection units 101, 106, and 111 respectively compare the transition data strings in Tables 1, 2, and 3 with the binarized data output from the maximum likelihood decoding unit 9. If the binarized data matches the transition data strings in Tables 1, 2, and 3 as a result of the comparison, the pattern detection units 101, 106, and 111 are most likely based on Tables 1, 2, and 3, respectively.
- the first state transition sequence and the second most likely second state transition sequence are selected.
- the difference metric calculation units 102, 107, and 112 determine the ideal value of the state transition sequence (PR equalization ideal values: see Table 1, Table 2, and Table 3). And a metric which is a distance between the digital reproduction signal and the digital reproduction signal. Further, the difference metric calculation units 102, 107, and 112 calculate the difference between the metrics calculated from the two state transition sequences, and perform absolute value processing on the metric difference having the plus or minus value.
- the outputs from the difference metric calculation units 102, 107, and 112 are input to the magnitude determination units 103, 108, and 113, respectively.
- the magnitude determination units 103, 108, and 113 compare the difference metric calculated by the difference metric calculation units 102, 107, and 112 with a predetermined value (signal processing threshold), respectively.
- Each of the pattern count units 104, 109, and 114 counts the number of differential metrics that are less than or equal to the signal processing threshold. These count values are the frequency of occurrence of each pattern group when calculating the error rate.
- the accumulation units 105, 110, and 115 each accumulate difference metrics that are equal to or less than the signal processing threshold. When the integrated values obtained by the integrating units 105, 110, and 115 are divided by the number of occurrences of patterns, an average value of difference metrics that are equal to or less than the signal processing threshold value can be obtained.
- Each integrating unit integrates the difference metric below the signal processing threshold, and each calculating unit divides each integrated value by the number of occurrences of the pattern to obtain the average value of the difference metric below the signal processing threshold.
- Each integrating unit may integrate the difference metric less than the signal processing threshold, and each calculating unit may divide each integrated value by the number of occurrences of the pattern to obtain the average value of the difference metric less than the signal processing threshold.
- the error calculation units 116, 117, and 118 calculate a predicted error rate from each integrated value of the difference metric equal to or less than the signal processing threshold and the number of pattern occurrences.
- the error rates calculated by these error calculators 116, 117, and 118 are added by an adder 119.
- the standard deviation corresponding to the error rate is calculated by the standard deviation calculation unit 120, and this becomes a signal index value for evaluating the signal quality.
- the process by the signal evaluation index detection unit 300 will be described in detail below.
- the reproduction signal reproduced from the information recording medium 1 by the PRML process is output as a binarized signal from the maximum likelihood decoding unit 9 and input to the signal evaluation index detection unit 300 as described above.
- the PR equalization ideal values of the first state transition string and the second state transition string are determined. For example, in Table 1, when (0, 0, 0, 0, X, 1, 1, 0, 0) is decoded as a binarized signal, the most probable first state transition sequence is (S0 , S1, S2, S3, S5, S6) and (S0, S0, S1, S2, S9, S6) are selected as the second most probable second state transition sequence.
- the PR equalization ideal value corresponding to the first state transition sequence is (1, 3, 5, 6, 5).
- the PR equalization ideal value corresponding to the second state transition sequence is (0, 1, 3, 4, 4).
- FIG. 8 is a distribution diagram of the difference metric in the signal processing of the PR12221ML system.
- the horizontal axis represents the difference metric
- the vertical axis represents the frequency of a predetermined difference metric value.
- a distribution with a smaller difference metric (square of Euclidean distance) indicates that there is a possibility of an error in signal processing by the PR12221ML method. From the graph of FIG. 8, it can be seen that the difference metric has a group of distributions in the portions of 12 and 14, and the difference metric higher than that is only 30 or more. That is, in order to obtain a signal index having a high correlation with the error rate, it can be seen that it is sufficient to focus on the group of difference metrics 12 and 14.
- the pattern detection units 101, 106, and 111 identify these state transition sequence patterns.
- the operation of the difference metric calculation unit that calculates the metric difference from the identified state transition sequence pattern will be described in more detail below.
- the distribution of (A) in FIG. 10 shows the output frequency distribution of the difference metric calculation unit 102
- the distribution of (B) in FIG. 10 shows the output frequency distribution of the difference metric calculation unit 107
- (C) in FIG. Indicates the output frequency distribution of the difference metric calculation unit 112.
- the processing of the difference metric calculation unit 107 is shown in equations (12) to (14)
- the processing of the difference metric calculation unit 112 is shown in equations (15) to (17).
- the state transition sequence pattern in Table 1 in which the square of the Euclidean distance is 14 is a pattern in which a 1-bit error occurs.
- the state transition sequence pattern in Table 2 in which the square of Euclidean distance is 12 is a pattern in which a 2-bit error occurs, and the state transition sequence pattern in Table 3 in which the square of Euclidean distance is 12 has a 3-bit error. It is a pattern that occurs.
- an error pattern in which the square of the Euclidean distance is 12 depends on the number of 2T continuations.
- Table 3 does not support 6-bit errors in which 2T is an error continuously. However, if necessary, a pattern for evaluating 2T continuous errors may be defined to extend the evaluation target pattern table.
- the error occurrence probability in the recording modulation code sequence is different in the state transition sequence pattern of each table.
- the state transition sequence pattern of Table 1 is about 40% for all samples
- the state transition sequence pattern of Table 2 is about 15% for all samples
- the state transition sequence pattern of Table 3 is about all samples.
- the occurrence frequency is about 5%.
- the prediction error rate may not be obtained appropriately depending on the shape of the distribution. Therefore, in the present embodiment, the calculation accuracy of the prediction error rate is improved by calculating the standard deviation ⁇ from the average value of the portion below the predetermined threshold (signal processing threshold) in the distribution to obtain the error rate.
- D 14 , D 12A, and D 12B which are outputs from the difference metric calculation units 102, 107, and 112, are input to the magnitude determination units 103, 108, and 113, respectively, and compared with predetermined values (signal processing threshold values).
- the signal processing threshold value for D 14 is set to 14
- the signal processing threshold values for D 12A and D 12B are both set to 12.
- the magnitude determination units 103, 108, 113 output a value if the difference metric is equal to or smaller than the signal processing threshold, and count up the count values of the pattern count units 104, 109, 114 corresponding to the respective pattern counts.
- the integration units 105, 110, and 115 integrate the difference metrics that are equal to or less than the signal processing threshold.
- the error calculation units 116, 117, and 118 calculate a predicted error rate from the integrated value of the difference metric that is equal to or smaller than the signal processing threshold and the number of pattern occurrences. The operation of these error calculation units 116, 117, and 118 will be described below.
- the average value of the metric can be obtained. Assuming that the average value of the difference metric below this signal processing threshold is M (x), the average value of the distribution function is ⁇ , the standard deviation is ⁇ n , the probability density function is f, and the distribution function is a normal distribution, The absolute value average value m of the difference metric less than or equal to the processing threshold is expressed by the following equation (18).
- the difference metric absolute value average value m below the signal processing threshold is obtained and then about 1.253 times. You can see that Since the signal processing threshold is fixed, the standard deviation ⁇ n can be calculated from the absolute value average value m. Then, the probability of occurrence of an error (error rate bER) calculated by the error calculators 116, 117, and 118 can be obtained from the following equation (20).
- d in the equation (20) is between the ideal signal of the most probable first state transition sequence in the state transition pattern to be extracted and the ideal signal of the second most probable second state transition sequence.
- p 14 , p 12A , and p 12B are error occurrence probabilities in the distribution components for all channel points.
- the error that occurs in the state transition sequence pattern of Table 1 is a 1-bit error, so 1 is generated, the error that occurs in the state transition sequence pattern of Table 2 is a 2-bit error, and 2 is the state transition sequence of Table 3. Since the error that occurs in the pattern is a 3-bit error, each is multiplied by 3. By adding these error rates, the probability of occurrence of an error in all patterns of the state transition sequence pattern in Table 1, the state transition sequence pattern in Table 2, and the state transition sequence pattern in Table 3 can be obtained. If the error occurrence probability is bER all, it can be expressed by equation (24).
- the standard deviation calculation unit 120 performs conversion from the bit error rate obtained by the equation (24) to the signal index value in order to obtain an index that can be handled in the same manner as jitter.
- p is the sum of p 14 , p 12A and p 12B , and erfc () is the integral value of the complementary error function.
- the definition formula of the signal index M of the present invention is Expression (26)
- the index value M can be obtained by substituting bER all calculated by Expression (24) into Expression (25).
- the virtual standard deviation ⁇ was calculated from the predicted error rate using the equations (20) to (26), and the signal index value M was calculated.
- the method of calculating the evaluation index M of the present embodiment is not limited to the above method, and other defining formulas may be used. An example of other definition formulas will be described below.
- the probability that the pattern Pa is detected as the pattern Pb is an error function of the following equation (27).
- t in the formula (27) represents a pattern number in Tables 1 to 3.
- d indicates the Euclidean distance in each pattern group in Tables 1 to 3. Specifically, d 2 is 14 in the case of the pattern group in Table 1, and d 2 is 12 in the case of the pattern group in Tables 2 and 3.
- the error occurrence probability occurring in all the patterns in the pattern group in Table 1, the pattern group in Table 2, and the pattern group in Table 3 can be calculated by the following formula (28) using the formula (27).
- N 1 , N 2 , and N 3 are the numbers of occurrences of the pattern groups defined in Table 1, Table 2, and Table 3, respectively.
- Equation (24) is calculated as an error rate with all channels including the evaluation pattern as parameters.
- Expression (28) is calculated as an error rate with the evaluation pattern as a parameter.
- the virtual standard deviation ⁇ can be calculated from the following equation (29).
- Equation (30) E ⁇ 1 means the inverse function of Equation (30).
- the evaluation index M can be calculated by the following equation (31) by normalizing with the detected window.
- the above formula (26) and the above formula (31) calculate the virtual ⁇ generated by the evaluation pattern defined in Tables 1 to 3, and therefore, the index value M is calculated as substantially the same value. Is done. The only difference is the evaluation parameter for calculating the error rate during the calculation and the detection window. Either equation may be used to calculate the signal index value M.
- the calculation of the signal index value M using the above equation (31) can also be applied to the first embodiment in which only a specific state transition pattern is to be extracted.
- FIG. 12 is an example of a simulation result showing a bit error rate (bER) and a signal index value [%] of Expression (18) when a reproduction stress such as tilt, defocus, and spherical aberration is applied.
- bER bit error rate
- ⁇ (black triangle) mark indicates defocus stress
- ⁇ (black circle) mark indicates spherical aberration stress
- ⁇ (black rhombus) mark indicates radial tilt stress
- ⁇ (black square) mark indicates tangential tilt stress. Show.
- the solid line in the figure is a theoretical curve.
- the signal index value for realizing the bER is about 15 [%].
- the signal index value M defined in the present embodiment is an error rate theoretical curve in the region of signal index value M ⁇ 15 [%] actually used in the system. Consistent. Therefore, it can be said that the signal evaluation method and the index according to the present embodiment are very effective from the viewpoint of appropriately evaluating the signal.
- the difference between a plurality of pattern groups having different occurrence probabilities and different numbers of errors to be generated One signal evaluation index is generated from the metric information. Specifically, the error rate predicted from the average value of the difference metric information equal to or less than the threshold value of the signal processing of each pattern group is obtained, the total is calculated, and the virtual normal distribution is calculated from the calculated total error rate. A standard deviation (hereinafter abbreviated as ⁇ ) is calculated, and a signal evaluation index including the standard deviation ⁇ of the normal distribution is generated. As a result, it is possible to provide a signal evaluation method and an evaluation index that are highly correlated with the error rate.
- the preamplifier unit 3, the AGC unit 4, and the waveform equalization unit 5 of the present embodiment shown in FIG. 2 may be configured by one analog integrated circuit (LSI).
- the preamplifier unit 3, the AGC unit 4, the waveform equalization unit 5, the A / D conversion unit 6, the PLL unit 7, the PR equalization unit 8, the maximum likelihood decoding unit 9, the signal evaluation index detection unit 100, and the optical disk controller unit 15 May be configured as one integrated circuit (LSI) mixed with analog and digital.
- the optical disc apparatus of the present invention is not limited to this and can be applied to a recording / reproducing apparatus.
- a circuit for recording is added, but since a known circuit configuration can be used, description thereof is omitted here.
- FIG. 13 is a block diagram showing a schematic configuration of the optical disc apparatus according to the present embodiment.
- the optical disk apparatus 600 includes an optical head unit 2, a preamplifier unit 3, an AGC (Automatic Gain Controller) unit 4, a waveform equalizing unit 5, an A / D conversion unit 6, a PLL (Phase Locked Loop) unit 7, and a PR equalizing unit 8.
- a maximum likelihood decoding unit 9 a signal evaluation index detection unit (reproduction signal evaluation device) 500, and an optical disk controller unit 15. Since the configuration and functions of these members constituting the optical disc apparatus 600 are the same as those in the first embodiment, description thereof is omitted here.
- the optical disc apparatus 600 includes a signal evaluation index detection unit 500 as a reproduction signal evaluation apparatus.
- the signal evaluation index detection unit 500 has the same configuration as that of the signal evaluation index detection unit 100 of the first embodiment except for setting the signal processing threshold. Therefore, components having the same configurations and functions as those of the signal evaluation index detection unit 100 of the first embodiment are denoted by the same reference numerals and description thereof is omitted.
- the signal evaluation index detection unit 500 includes an average value calculation unit 121 for calculating the average value of the output of the difference metric calculation unit 102 in addition to the configuration of the first embodiment.
- the code distance of the ideal signal (the ideal signal of the most probable first state transition sequence and the second most probable second state transition sequence in the specific state transition pattern to be extracted)
- the predetermined value of the square of the Euclidean distance between the ideal signal and the ideal signal is used. This is because when the recording is optimized, the average value of the outputs of the difference metric calculation unit matches the code distance of the ideal signal.
- the recording density of the optical disc further increases, there may be a case where the recording cannot be optimized at the position of the code distance of the ideal signal.
- the signal evaluation index detection unit 500 includes an average value calculation unit 121 for calculating the average value of the outputs of the difference metric calculation unit 102, and uses the average value as a signal processing threshold value to determine whether the size is large or small. 103 is input.
- the signal processing threshold can be appropriately set at the center of the distribution output from the difference metric calculation unit 102. Thereby, the correlation between the signal index value and the bit error rate when the recording density is increased can be improved as compared with the configuration of the first embodiment.
- the configuration of the present embodiment using the average value of the differential metric distribution as the signal processing threshold is particularly beneficial when a high-density recording medium is used as the information recording medium 1.
- FIG. 14 is a block diagram showing a schematic configuration of the optical disc apparatus according to the present embodiment.
- the optical disk apparatus 800 includes an optical head unit 2, a preamplifier unit 3, an AGC (Automatic Gain Controller) unit 4, a waveform equalization unit 5, an A / D conversion unit 6, a PLL (Phase Locked Loop) unit 7, and a PR equalization unit 8.
- the optical disc apparatus 800 includes a signal evaluation index detection unit 700 as a reproduction signal evaluation apparatus.
- the signal evaluation index detection unit 700 has the same configuration as that of the signal evaluation index detection unit 300 of the second embodiment except for the setting of the signal processing threshold. Therefore, components having the same configuration and function as those of the signal evaluation index detection unit 300 of the second embodiment are denoted by the same reference numerals and description thereof is omitted.
- the signal evaluation index detection unit 700 includes average value calculation units 121 and 122 for calculating average values of the outputs of the difference metric calculation units 102, 107, and 112. , 123 are provided.
- the code distance of the ideal signal (the most probable ideal signal of the first state transition sequence and the second most probable second state transition sequence in each state transition pattern to be extracted).
- a predetermined value (the square value of the Euclidean distance from the ideal signal) was used. This is because when the recording is optimized, the average value of the outputs of the difference metric calculation unit matches the code distance of the ideal signal.
- the recording density of the optical disc further increases, there may be a case where the recording cannot be optimized at the position of the code distance of the ideal signal.
- the signal evaluation index detection unit 700 includes average value calculation units 121, 122, and 123 for calculating the average value of the outputs of the difference metric calculation units 102, 107, and 112, and calculates the average value.
- the signal processing threshold is input to the magnitude determination units 103, 108, and 113.
- the signal processing threshold can be appropriately set at the center of the distribution output from the difference metric calculation units 102, 107, and 112. Thereby, the correlation between the signal index value and the bit error rate when the recording density is increased can be improved as compared with the configuration of the first embodiment.
- the configuration of the present embodiment using the average value of the differential metric distribution as the signal processing threshold is particularly beneficial when a high-density recording medium is used as the information recording medium 1.
- FIG. 15 is a block diagram showing a schematic configuration of the optical disc apparatus according to Embodiment 5 of the present invention.
- the optical disk device 920 includes an optical head unit 2, a preamplifier unit 3, an AGC unit 4, a waveform equalization unit 5, an A / D conversion unit 6, a PLL unit 7, a PR equalization unit 8, a maximum likelihood decoding unit 9, a signal evaluation index.
- a detection unit (reproduction signal evaluation device) 910 and an optical disk controller unit 15 are provided. Since the configuration and functions of some members constituting the optical disk device 920 are the same as those in Embodiments 1 to 4, description thereof is omitted here.
- the optical disc device 920 includes a signal evaluation index detection unit 910 as a reproduction signal evaluation device.
- the signal evaluation index detection unit 910 of the fifth embodiment has the same configuration except that the calculation processing for obtaining the standard deviation of the difference metric of the first and third embodiments is different. Therefore, components having the same configurations and functions as those of the signal evaluation index detection unit 100 of the first embodiment are denoted by the same reference numerals and description thereof is omitted.
- the signal evaluation index detection unit 910 determines whether the information recording medium 1 has a quality that conforms to a predetermined standard before shipping. It can be used as a reproduction signal evaluation device. In addition, the signal evaluation index detection unit 910 can be mounted on a driving device for the information recording medium 1 and used as an evaluation device for performing test recording before the user records information on the information recording medium 1. .
- the signal evaluation index detection unit 910 includes a pattern detection unit 101, a difference metric calculation unit 102, a magnitude determination unit 103, a pattern count unit 104, an integration unit 105, an error calculation unit 116, a pattern count unit 124, an integration unit 125, and a standard deviation calculation. Part 120 is provided.
- the signal evaluation index detection unit 910 includes an integration unit 125 for calculating an average value of the output of the difference metric calculation unit 102, and a difference metric calculation unit 102 And a pattern count unit 124 for counting outputs.
- the signal evaluation index detection unit 910 evaluates the quality of the reproduction signal based on the binarized signal generated using the PRML signal processing method from the reproduction signal reproduced from the information recording medium.
- the pattern detection unit 101 extracts a specific state transition pattern that may cause a bit error from the binarized signal.
- the difference metric calculating unit 102 is the most probable first state transition sequence ideal signal and reproduction signal corresponding to the binarized signal. And a difference metric that is a difference between the second metric between the ideal signal of the second most probable second state transition sequence corresponding to the binarized signal and the reproduced signal. .
- the integration unit 125 integrates the difference metric calculated by the difference metric calculation unit 102.
- the pattern count unit 124 counts the number of times of integration processing by the integration unit 125 by counting the number of occurrences of the pattern detection unit 101.
- the size determination unit 103 extracts a difference metric that is equal to or smaller than a predetermined signal processing threshold.
- the integrating unit 105 integrates the difference metrics that are equal to or smaller than the signal processing threshold extracted by the magnitude determining unit 103.
- the pattern count unit 104 counts the number of integration processes performed by the integration unit 105.
- the error calculation unit 116 includes an integration value integrated by the integration unit 125, a count value counted by the pattern count unit 124, an integration value integrated by the integration unit 105, and a count value counted by the pattern count unit 104. The error rate predicted based on the above is calculated.
- the error calculation unit 116 calculates the average value of the difference metric calculated based on the integrated value integrated by the integrating unit 125 and the count value counted by the pattern count unit 124, and the integrated value integrated by the integrating unit 105. And an error rate is calculated based on a predetermined calculation result based on the count value counted by the pattern count unit 104.
- the error calculation unit 116 includes the integration value integrated by the integration unit 125, the count value counted by the pattern count unit 124, the integration value integrated by the integration unit 105, and the count value counted by the pattern count unit 104. Is used to calculate the standard deviation of the difference metric that is equal to or less than the average value of the difference metric output, and the error rate is calculated from the standard deviation.
- the linear expression is an approximate expression calculated using an iterative process using the Newton method.
- the standard deviation calculator 120 calculates a standard deviation based on the error rate calculated by the error calculator 116.
- Pattern count section 124 counts the number of occurrences of the particular pattern to be detected by the pattern detection unit 101, and outputs the count value N 1.
- Integrating unit 125 integrates the output from the differential metric computing section 102, and outputs the integrated value S 1.
- the integrating unit 105 integrates the output results of the magnitude determining unit 103 and outputs an integrated value JS 1 .
- the pattern count unit 104 counts the number of times the condition is met in the size determination unit 103 and outputs a count value JN 1 . Since the configuration other than the integration unit 125 and the pattern count unit 124 for calculating the average value of the difference metric of each pattern group is exactly the same as that of the first embodiment, detailed operation description is omitted.
- the signal evaluation index detection unit 910 corresponds to an example of a reproduction signal evaluation device
- the pattern detection unit 101 corresponds to an example of a pattern extraction unit
- the difference metric calculation unit 102 corresponds to a difference metric calculation unit.
- the integration unit 125 corresponds to an example of a first integration unit
- the pattern count unit 124 corresponds to an example of a first count unit
- the size determination unit 103 corresponds to an example of a difference metric extraction unit.
- the integration unit 105 corresponds to an example of a second integration unit
- the pattern count unit 104 corresponds to an example of a second count unit
- the error calculation unit 116 corresponds to an example of an error rate calculation unit
- the standard deviation The calculation unit 120 corresponds to an example of a standard deviation calculation unit.
- the average value of the difference metric is obtained for the case where the average value of the output of the difference metric calculation unit does not match the code distance of the ideal signal.
- a configuration is proposed in which the prediction error rate is calculated from the standard deviation of the difference metric obtained based on the average value, and the correlation between the actually generated error rate and the signal index value is improved.
- a predetermined fixed value called a code distance of the ideal signal is used as a signal processing threshold in the process of obtaining the standard deviation from the difference metric output, as in the first embodiment.
- FIG. 16A and FIG. 16B are distribution diagrams showing the range of the difference metric in a certain recording state.
- the distributions in FIGS. 16A and 16B are examples in the case where the average value of the difference metric output does not match the code distance of the ideal signal.
- FIG. 16A is a distribution diagram showing the range of the difference metric used for obtaining the standard deviation in the third and fourth embodiments.
- a reproduction signal evaluation method that does not depend on recording quality is obtained by calculating an average value of distribution, calculating a standard deviation from a difference metric value of a portion smaller than the average value, and calculating a prediction error rate.
- the fifth embodiment aims to obtain the same effect as in the third and fourth embodiments by obtaining a standard deviation using a fixed signal processing threshold.
- FIG. 16B is a distribution diagram showing the range of the difference metric used for obtaining the standard deviation in the fifth embodiment.
- the standard deviation corresponding to the third and fourth embodiments can be obtained by applying a predetermined correction to the difference metric value of the portion smaller than the fixed signal processing threshold.
- S 1 is the integrated value of the differential metric
- N 1 is the frequency of the differential metric (a count value indicating the number of integrations of S 1 )
- JS 1 is the integrated value of the differential metric below the signal processing threshold (here, 0)
- JN 1 Is the frequency of the difference metric that is equal to or less than the signal processing threshold (here, 0) (a count value that represents the number of integrations of JS 1 )
- ⁇ is a predetermined frequency coefficient
- E 1 is an ideal signal processing value.
- FIG. 17A and FIG. 17B are diagrams for explaining a standard deviation calculation method in the fifth embodiment.
- the integrated value JS 1 needs to be normalized by the count value N 1 .
- a virtual standard deviation is obtained for the pattern group in Table 1.
- E 1 indicates a detection window, and 14 corresponding to the pattern group in Table 1 is inserted.
- Count N 1 can be obtained by the following equation (32).
- count value JN 1 can be obtained by the following equation (33).
- the count value N 1, calculated to normalize the accumulated value JS 1 is the following formula (35).
- the above formula (39) is defined by the following formula (40) when defined as a function having the standard deviation ⁇ 1 and the variable b 1 as arguments.
- the following formula (42) can be defined as an index reflecting the average value deviation of the distribution as shown in FIG. 17B in the detection window.
- ⁇ 1/2 for the two variables a 1 and b 1 satisfying the above equation (42) is calculated by the Newton method.
- the Newton method is one of rooting algorithms based on an iterative method for solving an equation system by numerical calculation in the field of numerical analysis, and has been used in numerical calculation for a long time. Here, a description of the Newton algorithm will be omitted.
- Figure 18 is a variable a 1 (a x), the relationship between the standard deviation sigma 1/2 is calculated by Newton's method ( ⁇ x / 2), shown in each average deviation of the output of the differential metric FIG It is. 18, the horizontal axis represents the variable a 1 obtained from the above equation (37) (a x) [ %], and the vertical axis, calculated in Newton method ⁇ 1/2 ( ⁇ x / 2) [ %].
- the average deviation amount of the difference metric output is the variable b 1 obtained from the above equation (38).
- Relation ⁇ 1/2 [%] shown in FIG. 18 and the variable a 1 was a b 1 and a variable, it can be seen that expressed by primary linear equation. From this, ⁇ 1/2 obtained by the Newton method can be expressed by a linear expression in which the average value of the difference metric output is the variable b 1 .
- P is the slope in which the average value of the output of the differential metrics and variable b 1
- Q is the intercept in which the average value of the output of the differential metrics and variable b 1.
- the value of P values and Q may have a table for b 1 obtained by approximate calculation.
- Table 4 shows a specific example of a table that represents the value of P and the value of Q with the variable b x as an argument. Note that x in the variable b x in Table 4 means that the standard deviation ⁇ x is obtained for the pattern groups in Table 1, Table 2, and Table 3, respectively. Any one of “1”, “2”, and “3” corresponding to each of 3 is inserted.
- the correction table is uniquely defined in the correction range of ⁇ 30% to + 30%, but the correction range may be enlarged or reduced. Further, it is desirable that the correction range supports a range that takes into account the amount of deviation that actually occurs.
- the argument b x in the table of P (b x ) and Q (b x ) in Table 4 is expressed at 0.05 intervals.
- variables b x values between interval variables b x stored in advance (e.g., 0.025) is applied at a variable stored in 0.05 intervals shown in Table 4 b of x
- P corresponding to the front and rear of the variable b x of the input value (b x) and Q a (b x) may be respectively used by linear interpolation.
- P (b x ) and Q (b x ) corresponding to the variable b x closest to the input value may be selected from the variables b x stored in advance.
- the integrated value (JS 1 ) is based on the deviation (S 1 / N 1 ) of the distribution of the output of the difference metric calculation unit 102 and the fixed signal processing threshold. ) And the number of times of integration (JN 1 ), a correction calculation is performed to obtain a standard deviation ⁇ 1/2 that takes into account the average value deviation of the distribution.
- a simple linear expression represented by the above expression (42) is used as a correction expression for improving the prediction error rate calculation accuracy.
- the prediction error rate is obtained using the standard deviation ⁇ 1/2 obtained by the equation (42) according to any of the patterns in Tables 1 to 3.
- FIG. 19 is a block diagram illustrating a configuration of the optical disc device 940 according to the sixth embodiment.
- the information recording medium 1 is an information recording medium for optically recording and reproducing information, for example, an optical disk medium.
- the optical disk device 940 is a reproducing device that reproduces information with respect to the mounted information recording medium 1.
- the optical disk device 940 includes an optical head unit 2, a preamplifier unit 3, an AGC unit 4, a waveform equalization unit 5, an A / D conversion unit 6, a PLL unit 7, a PR equalization unit 8, a maximum likelihood decoding unit 9, a signal evaluation index.
- a detection unit (reproduction signal evaluation device) 930 and an optical disk controller unit 15 are provided. Since the configuration and functions of some members constituting the optical disk device 940 are the same as those in the first to fifth embodiments, the description thereof is omitted here.
- the signal evaluation index detection unit 930 determines whether or not the information recording medium 1 has a quality that conforms to a predetermined standard before shipment, as with the signal evaluation index detection units of the first to fifth embodiments. It can be used as a reproduction signal evaluation device. In addition, the signal evaluation index detection unit 930 can be mounted on a drive device of the information recording medium 1 and used as an evaluation device for performing test recording before the user records information on the information recording medium 1. .
- the signal evaluation index detection unit 930 includes pattern detection units 101, 106, 111, difference metric calculation units 102, 107, 112, size determination units 103, 108, 113, pattern count units 104, 109, 114, and integration units 105, 110. 115, error calculation units 116, 117, and 118, pattern count units 124, 126, and 128, integration units 125, 127, and 129, an addition unit 119, and a standard deviation calculation unit 120.
- the signal evaluation index detection unit 930 includes integration units 125, 127, and 129 for calculating average values of the outputs of the difference metric calculation units 102, 107, and 112. And pattern count units 124, 126, and 128 for counting the outputs of the difference metric calculation unit 102.
- the pattern detection units 101, 106, and 111 each extract a state transition pattern that may cause a bit error from the binarized signal.
- the difference metric calculation units 102, 107, and 112 for each state transition pattern extracted by the pattern detection units 101, 106, and 111, based on the binarized signal, the most probable first corresponding to the binarized signal. And a second metric between the ideal signal and the reproduced signal of the second most probable second state transition sequence corresponding to the binarized signal. A difference metric that is a difference from the metric is calculated.
- the accumulating units 125, 127, and 129 accumulate the differential metrics calculated by the differential metric calculating units 102, 107, and 112 for each state transition pattern.
- the pattern count units 124, 126, and 128 count the number of integration processes performed by the integration units 125, 127, and 129 for each state transition pattern.
- the size determination units 103, 108, and 113 extract a differential metric that is equal to or less than a predetermined signal processing threshold for each state transition pattern.
- the integration units 105, 110, and 115 integrate the difference metrics that are equal to or less than the signal processing threshold extracted for each state transition pattern by the size determination units 103, 108, and 113, respectively.
- the pattern count units 104, 109, and 114 count the number of integration processes performed by the integration units 105, 110, and 115 for each state transition pattern.
- the error calculation units 116, 117, and 118 include a plurality of integration values integrated by the integration units 125, 127, and 129, a plurality of count values counted by the pattern count units 124, 126, and 128, and the integration units 105 and 110, respectively. , 115 and a plurality of error rates predicted based on the plurality of integrated values counted by the pattern count units 104, 109, 114 are calculated for each state transition pattern.
- the standard deviation calculation unit 120 calculates a standard deviation based on the sum of a plurality of error rates calculated by the error calculation units 116, 117, and 118.
- the pattern count units 124, 126, and 128 count the number of occurrences of the specific pattern detected by the pattern detection units 101, 106, and 111, and output count values N 1 , N 2 , and N 3 .
- Integration units 125, 127, and 129 integrate the outputs of difference metric calculation units 102, 107, and 112, and output integrated values S 1 , S 2 , and S 3 .
- Integration units 105, 110, and 115 integrate the output results of magnitude determination units 103, 108, and 113, and output integrated values JS 1 , JS 2 , and JS 3 .
- the pattern count units 104, 109, and 114 count the number of condition adaptations in the size determination units 103, 108, and 113, and output count values JN 1 , JN 2 , and JN 3 . Since the configuration other than the integration units 125, 127, and 129 and the pattern count units 124, 126, and 128 for calculating the average value of the difference metric of each pattern group is exactly the same as that of the first embodiment, detailed operations are performed. Description is omitted.
- the signal evaluation index detection unit 930 corresponds to an example of a reproduction signal evaluation device
- the pattern detection units 101, 106, and 111 correspond to an example of a pattern extraction unit
- the difference metric calculation unit 102, 107 and 112 correspond to an example of a difference metric calculation unit
- integration units 125, 127, and 129 correspond to an example of a first integration unit
- pattern count units 124, 126, and 128 serve as an example of a first count unit.
- the magnitude determination units 103, 108, and 113 correspond to an example of a difference metric extraction unit
- the integration units 105, 110, and 115 correspond to an example of a second integration unit
- the pattern count units 104, 109, and 114 It corresponds to an example of a second count unit
- the error calculation units 116, 117, and 118 correspond to an example of an error rate calculation unit
- the standard deviation calculation unit 120 is a standard. It corresponds to an example of the deviation calculating section.
- the average value of the difference metric is obtained for the case where the average value of the output of the difference metric calculation unit does not match the code distance of the ideal signal.
- a configuration was proposed in which the prediction error rate was calculated from the standard deviation of the difference metric obtained based on the average value, and the correlation between the actually generated error rate and the signal index value was improved.
- the sixth embodiment in the same manner as in the first embodiment, in the process of obtaining the standard deviation from the output of the difference metric, a predetermined fixed value called the code distance of the ideal signal is used as the signal processing threshold. Use. Further, depending on the recording state (quality), when the average value of the outputs of the differential metric calculation units 102, 107, and 112 does not match the code distance of the ideal signal, the standard deviation error caused by the average value deviation is corrected. Then, a calculation method for solving the problem that the correlation between the signal index value and the bit error rate is insufficient is proposed.
- S x is the integrated value of the differential metric
- N x is the frequency of the differential metric (a count value indicating the number of integrations of S x )
- JS x is the integrated value of the differential metric equal to or less than the signal processing threshold (here, 0)
- JN x Is the frequency of the difference metric that is equal to or less than the signal processing threshold (here, 0) (a count value that represents the number of JS x integrations)
- ⁇ is a predetermined frequency coefficient
- E x is an ideal signal processing value.
- 20A and 20B are diagrams for explaining a standard deviation calculation method according to the sixth embodiment.
- x means obtaining a virtual standard deviation for each of the pattern groups in Tables 1, 2, and 3. Any value of “1”, “2”, and “3” corresponding to each of Table 1, Table 2, and Table 3 is inserted into x.
- Ex represents a detection window, and 14 is inserted for the pattern group of Table 1 and 12 is inserted for the pattern group of Table 2 and Table 3.
- the count value N x can be obtained by the following equation (43).
- count value JN x can be obtained by the following equation (44).
- the count value N x, calculated to normalize the accumulated value JS x is the following equation (46).
- Equation (50) is expressed by the following equation (51) when defined as a function having the standard deviation ⁇ x and the variable b x as arguments.
- the following formula (53) can be defined as an index in which the average value deviation of the distribution as shown in FIG. 20B is reflected in the detection window.
- the Newton method is one of rooting algorithms based on an iterative method for solving an equation system by numerical calculation in the field of numerical analysis, and has been used in numerical calculation for a long time. Here, a description of the Newton algorithm will be omitted.
- the average value deviation amount of the difference metric output is the variable b x obtained from the above equation (49). It can be seen that the relationship between ⁇ x / 2 [%] and the variable a x shown in FIG. 18 can be expressed by a linear expression using b x as a variable. From this, ⁇ x / 2 obtained by the Newton method can be expressed by a linear expression in which the average value of the difference metric output is the variable b x .
- P is a slope with the average value of the difference metric output as a variable b x
- Q is an intercept with the average value of the difference metric output as a variable b x
- the value of P values and Q may have a table for b x obtained by approximate calculation. That is, the standard deviation calculation unit 120 may store in advance a table that represents the value of P and the value of Q with the variable b x as an argument, as shown in Table 4 above.
- the integration is performed based on the deviation amount (S x / N x ) of the output distribution of the differential metric calculation units 102, 107, and 112 and the fixed signal processing threshold.
- a correction operation is performed to obtain a standard deviation ⁇ x / 2 taking into account the average value deviation of the distribution.
- a simple linear expression represented by the above expression (53) is used as a correction expression for improving the prediction error rate calculation accuracy.
- the prediction error rate is obtained using the standard deviation ⁇ x / 2 obtained by the equation (53) according to the pattern groups in Tables 1 to 3. Accordingly, even when the center of the distribution of the outputs of the difference metric calculation units 102, 107, and 112 is deviated from the signal processing threshold as shown in FIGS. 21B and 21C, a signal index value having a high correlation with the error rate is obtained. be able to.
- a reproduction signal evaluation method is a reproduction that evaluates the quality of a reproduction signal based on a binarized signal generated by using a PRML signal processing method from a reproduction signal reproduced from an information recording medium.
- a signal evaluation method for extracting a specific state transition pattern that may cause a bit error from the binarized signal, and the binary value of the state transition pattern extracted in the pattern extraction step Based on the binarized signal, the first metric between the ideal signal of the first state transition sequence most likely corresponding to the binarized signal and the reproduction signal, and the second corresponding to the binarized signal
- a difference metric calculation step for calculating a difference metric that is a difference between an ideal signal of the likely second state transition sequence and a second metric between the reproduced signal;
- a first integration step for integrating the difference metrics calculated in the difference metric calculation step; a first count step for counting the number of integration processes in the first integration step; and the predetermined signal processing threshold value or less
- a differential metric extraction step for extracting a differential metric,
- a specific state transition pattern that may cause a bit error is extracted from the binarized signal generated by reproducing the information recording medium.
- a state transition pattern that may cause a bit error is a state having a confluence path that can take a plurality of state transitions when transitioning from a predetermined state at a certain time to a predetermined state at another time.
- the transition pattern is a state transition pattern of a confluence path in which the Euclidean distance between the ideal signal of the most probable first state transition sequence and the ideal signal of the second most probable second state transition sequence is relatively small. is there.
- a difference metric that is a difference between the ideal signal of the second most probable second state transition sequence corresponding to the binarized signal and the second metric between the reproduced signal is calculated.
- the calculated difference metric is integrated and the difference metric integration processing count is counted. Also, differential metrics that are less than or equal to a predetermined signal processing threshold are extracted, the differential metrics that are less than or equal to the extracted signal processing threshold are integrated, and the number of integration processes of the difference metrics that are less than or equal to the signal processing threshold is counted.
- the calculated difference metric integration value, the difference metric integration processing count value, the difference metric integration value less than or equal to a predetermined signal processing threshold, and the difference metric integration processing less than or equal to a predetermined signal processing threshold A predicted error rate is calculated based on the count value of the number of times.
- a standard deviation is calculated based on the calculated error rate, and the quality of the reproduction signal is evaluated using the calculated standard deviation.
- the average value of the difference metric does not match the code distance of the ideal signal, the error of the standard deviation that occurs when the average value of the difference metric deviates from the code distance of the ideal signal is calculated.
- the difference metric integration value and the difference metric integration processing count value By correcting the difference metric integration value and the difference metric integration processing count value, the correlation between the error rate and the signal index value is improved, and the quality of the reproduction signal of the information recording medium is improved. It can be evaluated with high accuracy.
- the signal processing threshold is a Euclidean between the ideal signal of the most probable first state transition sequence and the ideal signal of the second most probable second state transition sequence. A square value of the distance is preferable.
- the signal processing threshold corresponding to the specific state transition pattern to be extracted is matched with the Euclidean distance between the ideal signal of the first state transition sequence and the ideal signal of the second state transition sequence. It can be set accurately. This is particularly effective when evaluating a signal in which a plurality of state transition patterns that may cause an error are mixed.
- the error rate calculating step includes an integrated value integrated in the first integrating step, a count value counted in the first counting step, and the second integrated value. Calculate a standard deviation of the difference metric that is equal to or less than the average value of the difference metric output, using a linear expression with the accumulated value accumulated in the step and the count value counted in the second counting step as arguments; It is preferable to calculate the error rate from the standard deviation.
- the calculated integrated value of the differential metric, the count value of the differential metric integrated processing count, the integrated value of the differential metric that is equal to or smaller than the predetermined signal processing threshold, and the differential metric that is equal to or smaller than the predetermined signal processing threshold can be calculated using a linear expression using the count value of the number of integration processes as an argument, and the error rate can be calculated from the standard deviation.
- the linear expression is an approximate expression calculated using an iterative process by Newton's method.
- the linear expression used when calculating the error rate can be expressed by an approximate expression calculated using an iterative process using the Newton method.
- the error rate calculation step includes calculating the difference calculated based on the integrated value integrated in the first integration step and the count value counted in the first count step. Calculating the error rate based on a metric average value, a predetermined calculation result based on the integrated value integrated in the second integrating step and the count value counted in the second counting step; Is preferred.
- the difference metric average value calculated based on the calculated difference metric integration value and the difference metric integration processing count value, and the difference metric integration value equal to or less than a predetermined signal processing threshold The error rate can be calculated based on a predetermined calculation result based on the count value of the number of times of difference metric integration processing equal to or less than a predetermined signal processing threshold.
- a reproduction signal evaluation method evaluates the quality of a reproduction signal based on a binary signal generated from a reproduction signal reproduced from an information recording medium using a PRML signal processing method.
- a reproduction signal evaluation method wherein a pattern extraction step for extracting a plurality of state transition patterns that may cause a bit error from the binarized signal, and for each state transition pattern extracted in the pattern extraction step, Based on the binarized signal, a first metric between the most probable first state transition sequence ideal signal corresponding to the binarized signal and the reproduction signal, and corresponding to the binarized signal
- a difference metric for calculating a difference metric that is a difference between an ideal signal of the second most probable second state transition sequence and a second metric between the reproduced signals.
- An output step a first integration step for integrating the difference metrics calculated in the difference metric calculation step for each state transition pattern; and a number of integration processes in the first integration step for each state transition pattern.
- a second integration step for integrating each difference metric less than or equal to the signal processing threshold; a second count step for counting the number of integration processes in the second integration step for each state transition pattern; and the first Integrated in the integration step A plurality of integrated values, a plurality of counted values counted in the first counting step, a plurality of integrated values integrated in the second integrating step, and a plurality of counted values counted in the second counting step
- An error rate calculation step for calculating a plurality of error rates predicted based on the count value for each state transition pattern, and a standard deviation based on a sum of the plurality of error rates calculated in the error rate calculation step A standard deviation calculating step for calculating, and an evaluation step for evaluating the quality of the reproduction signal using the standard deviation calculated in the standard deviation calculating step.
- a plurality of state transition patterns that may cause a bit error are extracted from the binarized signal generated by reproducing the information recording medium. Then, for each extracted state transition pattern, based on the binarized signal, a first metric between the ideal signal and the reproduced signal of the most probable first state transition sequence corresponding to the binarized signal; A difference metric that is a difference between the ideal signal of the second most probable second state transition sequence corresponding to the binarized signal and the second metric between the reproduced signal is calculated.
- the calculated difference metric is integrated for each state transition pattern, and the number of difference metric integration processes is counted for each state transition pattern.
- a difference metric less than or equal to a predetermined signal processing threshold is extracted for each state transition pattern, and a difference metric less than or equal to the signal processing threshold extracted for each state transition pattern is integrated, respectively.
- the number of integration processes is counted for each state transition pattern.
- a plurality of calculated difference metrics, a plurality of difference metric integration counts, a plurality of difference metric integration values less than or equal to a predetermined signal processing threshold, and a predetermined signal processing threshold or less A plurality of error rates predicted based on a plurality of count values of the number of integration processes of the difference metric are calculated for each state transition pattern.
- a standard deviation is calculated based on the calculated sum of the error rates, and the quality of the reproduction signal is evaluated using the calculated standard deviation.
- the average value of the difference metric does not match the code distance of the ideal signal, the error of the standard deviation that occurs when the average value of the difference metric deviates from the code distance of the ideal signal is calculated.
- the difference metric integration value and the difference metric integration processing count value By correcting the difference metric integration value and the difference metric integration processing count value, the correlation between the error rate and the signal index value is improved, and the quality of the reproduction signal of the information recording medium is improved. It can be evaluated with high accuracy.
- a reproduction signal evaluation apparatus evaluates the quality of a reproduction signal based on a binarized signal generated from a reproduction signal reproduced from an information recording medium using a PRML signal processing method.
- a reproduction signal evaluation apparatus wherein a pattern extraction unit extracts a specific state transition pattern that may cause a bit error from the binarized signal, and the state transition pattern extracted by the pattern extraction unit is 2 Based on the binarized signal, the first metric between the ideal signal of the first state transition sequence most likely corresponding to the binarized signal and the reproduction signal, and the second metric corresponding to the binarized signal
- a difference metric calculation unit that calculates a difference metric that is a difference between an ideal signal of the second state transition sequence that is most likely and a second metric between the reproduced signal, and the difference metric
- a first integration unit that integrates the difference metric calculated by the first calculation unit, a first count unit that counts the number of integration processes performed by the first integration unit, and the difference metric that is less than or equal to a predetermined signal
- a specific state transition pattern that may cause a bit error is extracted from the binarized signal generated by reproducing the information recording medium.
- a state transition pattern that may cause a bit error is a state having a confluence path that can take a plurality of state transitions when transitioning from a predetermined state at a certain time to a predetermined state at another time.
- the transition pattern is a state transition pattern of a confluence path in which the Euclidean distance between the ideal signal of the most probable first state transition sequence and the ideal signal of the second most probable second state transition sequence is relatively small. is there.
- a difference metric that is a difference between the ideal signal of the second most probable second state transition sequence corresponding to the binarized signal and the second metric between the reproduced signal is calculated.
- the calculated difference metric is integrated and the difference metric integration processing count is counted. Also, differential metrics that are less than or equal to a predetermined signal processing threshold are extracted, the differential metrics that are less than or equal to the extracted signal processing threshold are integrated, and the number of integration processes of the difference metrics that are less than or equal to the signal processing threshold is counted.
- the calculated difference metric integration value, the difference metric integration processing count value, the difference metric integration value less than or equal to a predetermined signal processing threshold, and the difference metric integration processing less than or equal to a predetermined signal processing threshold A predicted error rate is calculated based on the count value of the number of times.
- a standard deviation is calculated based on the calculated error rate, and the quality of the reproduction signal is evaluated using the calculated standard deviation.
- the average value of the difference metric does not match the code distance of the ideal signal, the error of the standard deviation that occurs when the average value of the difference metric deviates from the code distance of the ideal signal is calculated.
- the difference metric integration value and the difference metric integration processing count value By correcting the difference metric integration value and the difference metric integration processing count value, the correlation between the error rate and the signal index value is improved, and the quality of the reproduction signal of the information recording medium is improved. It can be evaluated with high accuracy.
- the signal processing threshold value is a Euclidean between the ideal signal of the most probable first state transition sequence and the ideal signal of the second most probable second state transition sequence. A square value of the distance is preferable.
- the signal processing threshold corresponding to the specific state transition pattern to be extracted is matched with the Euclidean distance between the ideal signal of the first state transition sequence and the ideal signal of the second state transition sequence. It can be set accurately. This is particularly effective when evaluating a signal in which a plurality of state transition patterns that may cause an error are mixed.
- the error rate calculation unit includes an integration value integrated by the first integration unit, a count value counted by the first count unit, and the second integration. Calculating a standard deviation of a difference metric that is equal to or less than an average value of the difference metric output, using a linear expression that uses as an argument the integrated value accumulated by the unit and the count value counted by the second count unit; It is preferable to calculate the error rate from the standard deviation.
- the calculated integrated value of the differential metric, the count value of the differential metric integrated processing count, the integrated value of the differential metric that is equal to or smaller than the predetermined signal processing threshold, and the differential metric that is equal to or smaller than the predetermined signal processing threshold can be calculated using a linear expression using the count value of the number of integration processes as an argument, and the error rate can be calculated from the standard deviation.
- the linear expression is preferably an approximate expression calculated using an iterative process using a Newton method.
- the linear expression used when calculating the error rate can be expressed by an approximate expression calculated using an iterative process using the Newton method.
- the error rate calculation unit calculates the difference calculated based on the integrated value integrated by the first integrating unit and the count value counted by the first counting unit. Calculating the error rate based on a metric average value, a predetermined calculation result based on the integrated value integrated by the second integrating unit and the count value counted by the second counting unit; Is preferred.
- the difference metric average value calculated based on the calculated difference metric integration value and the difference metric integration processing count value, and the difference metric integration value equal to or less than a predetermined signal processing threshold The error rate can be calculated based on a predetermined calculation result based on the count value of the number of times of difference metric integration processing equal to or less than a predetermined signal processing threshold.
- a reproduction signal evaluation apparatus evaluates the quality of a reproduction signal based on a binarized signal generated from a reproduction signal reproduced from an information recording medium using a PRML signal processing method.
- a pattern extraction unit that extracts a plurality of state transition patterns that may cause a bit error from the binarized signal, and for each state transition pattern extracted by the pattern extraction unit, Based on the binarized signal, a first metric between the most probable first state transition sequence ideal signal corresponding to the binarized signal and the reproduction signal, and corresponding to the binarized signal
- a difference metric calculation unit for calculating a difference metric that is a difference between an ideal signal of the second most probable second state transition sequence and a second metric between the reproduced signal,
- a first integration unit that integrates the difference metric calculated by the difference metric calculation unit for each state transition pattern, and a first count that counts the number of integration processes by the first integration unit for each state transition pattern
- a difference metric extraction unit that extracts the difference metric less than or
- An error rate calculation unit that calculates a plurality of error rates predicted for each state transition pattern based on the plurality of integrated values and the plurality of count values counted by the second count unit, and the error A standard deviation calculating unit that calculates a standard deviation based on a sum of the plurality of error rates calculated by the rate calculating unit.
- a plurality of state transition patterns that may cause a bit error are extracted from the binarized signal generated by reproducing the information recording medium. Then, for each extracted state transition pattern, based on the binarized signal, a first metric between the ideal signal and the reproduced signal of the most probable first state transition sequence corresponding to the binarized signal; A difference metric that is a difference between the ideal signal of the second most probable second state transition sequence corresponding to the binarized signal and the second metric between the reproduced signal is calculated.
- the calculated difference metric is integrated for each state transition pattern, and the number of difference metric integration processes is counted for each state transition pattern.
- a difference metric less than or equal to a predetermined signal processing threshold is extracted for each state transition pattern, and a difference metric less than or equal to the signal processing threshold extracted for each state transition pattern is integrated, respectively.
- the number of integration processes is counted for each state transition pattern.
- a plurality of calculated difference metrics, a plurality of difference metric integration counts, a plurality of difference metric integration values less than or equal to a predetermined signal processing threshold, and a predetermined signal processing threshold or less A plurality of error rates predicted based on a plurality of count values of the number of integration processes of the difference metric are calculated for each state transition pattern.
- a standard deviation is calculated based on the calculated sum of the error rates, and the quality of the reproduction signal is evaluated using the calculated standard deviation.
- the average value of the difference metric does not match the code distance of the ideal signal, the error of the standard deviation that occurs when the average value of the difference metric deviates from the code distance of the ideal signal is calculated.
- the difference metric integration value and the difference metric integration processing count value By correcting the difference metric integration value and the difference metric integration processing count value, the correlation between the error rate and the signal index value is improved, and the quality of the reproduction signal of the information recording medium is improved. It can be evaluated with high accuracy.
- An optical disc apparatus includes a reproducing unit that generates a binarized signal using a PRML signal processing method from a reproduction signal obtained by reproducing an optical disc that is an information recording medium.
- a reproduction signal evaluation apparatus as described above. According to this configuration, the reproduction signal evaluation apparatus described above can be applied to an optical disc apparatus.
- the present invention is particularly useful in the technical field of performing signal processing using the maximum likelihood decoding method.
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Abstract
Description
本発明の一実施の形態に係る再生信号評価装置を備えた光ディスク装置について、図面を参照し以下に説明する。図1は、実施の形態1の光ディスク装置200の構成を示すブロック図である。
本発明の他の実施の形態に係る再生信号評価装置を備えた光ディスク装置について、図面を参照し以下に説明する。なお、実施の形態1と同様の構成については、同じ部材番号を付記し、その説明を適宜省略する。図2は、実施の形態2の光ディスク装置400の構成を示すブロック図である。
次に、本発明のさらに他の実施の形態に係る光ディスク装置について図面を参照し、以下に説明する。
次に、本発明のさらに他の実施の形態に係る光ディスク装置について図面を参照し、以下に説明する。
次に、本発明の実施の形態5に係る光ディスク装置を説明する。図15は、本発明の実施の形態5に係る光ディスク装置の概略構成を示すブロック図である。
本発明の実施の形態6に係る再生信号評価装置を備えた光ディスク装置について、図面を参照し以下に説明する。なお、実施の形態5と同様の構成については、同じ部材番号を付記し、その説明を適宜省略する。図19は、実施の形態6の光ディスク装置940の構成を示すブロック図である。
Claims (13)
- 情報記録媒体から再生された再生信号からPRML信号処理方式を用いて生成された2値化信号に基づいて、当該再生信号の品質を評価する再生信号評価方法であって、
前記2値化信号から、ビットエラーを引き起こす可能性のある特定の状態遷移パターンを抽出するパターン抽出ステップと、
前記パターン抽出ステップにおいて抽出された状態遷移パターンの前記2値化信号に基づいて、当該2値化信号に対応する最も確からしい第1の状態遷移列の理想信号と前記再生信号との間の第1メトリックと、当該2値化信号に対応する2番目に確からしい第2の状態遷移列の理想信号と前記再生信号との間の第2メトリックとの差分である差分メトリックを算出する差分メトリック算出ステップと、
前記差分メトリック算出ステップにおいて算出された前記差分メトリックを積算する第1の積算ステップと、
前記第1の積算ステップにおける積算処理回数をカウントする第1のカウントステップと、
所定の信号処理閾値以下の前記差分メトリックを抽出する差分メトリック抽出ステップと、
前記差分メトリック抽出ステップにおいて抽出された前記信号処理閾値以下の差分メトリックを積算する第2の積算ステップと、
前記第2の積算ステップにおける積算処理回数をカウントする第2のカウントステップと、
前記第1の積算ステップにおいて積算された積算値と、前記第1のカウントステップにおいてカウントされたカウント値と、前記第2の積算ステップにおいて積算された積算値と、前記第2のカウントステップにおいてカウントされたカウント値とに基づいて予測されるエラーレートを算出するエラーレート算出ステップと、
前記エラーレート算出ステップにおいて算出された前記エラーレートに基づいて標準偏差を算出する標準偏差算出ステップと、
前記標準偏差算出ステップにおいて算出された前記標準偏差を用いて前記再生信号の品質を評価する評価ステップとを含む再生信号評価方法。 - 前記信号処理閾値は、前記最も確からしい第1の状態遷移列の理想信号と前記2番目に確からしい第2の状態遷移列の理想信号との間のユークリッド距離の2乗値である請求項1記載の再生信号評価方法。
- 前記エラーレート算出ステップは、前記第1の積算ステップにおいて積算された積算値と、前記第1のカウントステップにおいてカウントされたカウント値と、前記第2の積算ステップにおいて積算された積算値と、前記第2のカウントステップにおいてカウントされたカウント値とを引数とした一次式を用いて、前記差分メトリック出力の平均値以下の差分メトリックの標準偏差を算出し、その標準偏差から前記エラーレートを算出する請求項1又は2記載の再生信号評価方法。
- 前記一次式は、ニュートン法による反復処理を用いて算出される近似式である請求項3記載の再生信号評価方法。
- 前記エラーレート算出ステップは、前記第1の積算ステップにおいて積算された積算値と前記第1のカウントステップにおいてカウントされたカウント値とに基づいて算出した前記差分メトリックの平均値と、前記第2の積算ステップにおいて積算された積算値と前記第2のカウントステップにおいてカウントされたカウント値とに基づく所定の演算結果とに基づいて、前記エラーレートを算出する請求項1~4のいずれかに記載の再生信号評価方法。
- 情報記録媒体から再生された再生信号からPRML信号処理方式を用いて生成された2値化信号に基づいて、当該再生信号の品質を評価する再生信号評価方法であって、
前記2値化信号から、ビットエラーを引き起こす可能性のある複数の状態遷移パターンを抽出するパターン抽出ステップと、
前記パターン抽出ステップにおいて抽出された状態遷移パターン毎に、前記2値化信号に基づいて、当該2値化信号に対応する最も確からしい第1の状態遷移列の理想信号と前記再生信号との間の第1メトリックと、当該2値化信号に対応する2番目に確からしい第2の状態遷移列の理想信号と前記再生信号との間の第2メトリックとの差分である差分メトリックをそれぞれ算出する差分メトリック算出ステップと、
前記差分メトリック算出ステップにおいて算出された前記差分メトリックを前記状態遷移パターン毎にそれぞれ積算する第1の積算ステップと、
前記第1の積算ステップにおける積算処理回数を前記状態遷移パターン毎にカウントする第1のカウントステップと、
所定の信号処理閾値以下の前記差分メトリックを前記状態遷移パターン毎にそれぞれ抽出する差分メトリック抽出ステップと、
前記差分メトリック抽出ステップにおいて前記状態遷移パターン毎にそれぞれ抽出された前記信号処理閾値以下の差分メトリックをそれぞれ積算する第2の積算ステップと、
前記第2の積算ステップにおける積算処理回数を前記状態遷移パターン毎にカウントする第2のカウントステップと、
前記第1の積算ステップにおいて積算された複数の積算値と、前記第1のカウントステップにおいてカウントされた複数のカウント値と、前記第2の積算ステップにおいて積算された複数の積算値と、前記第2のカウントステップにおいてカウントされた複数のカウント値とに基づいて予測される複数のエラーレートを前記状態遷移パターン毎に算出するエラーレート算出ステップと、
前記エラーレート算出ステップにおいて算出された前記複数のエラーレートの総和に基づいて標準偏差を算出する標準偏差算出ステップと、
前記標準偏差算出ステップにおいて算出された前記標準偏差を用いて前記再生信号の品質を評価する評価ステップとを含む再生信号評価方法。 - 情報記録媒体から再生された再生信号からPRML信号処理方式を用いて生成された2値化信号に基づいて、当該再生信号の品質を評価する再生信号評価装置であって、
前記2値化信号から、ビットエラーを引き起こす可能性のある特定の状態遷移パターンを抽出するパターン抽出部と、
前記パターン抽出部によって抽出された状態遷移パターンの前記2値化信号に基づいて、当該2値化信号に対応する最も確からしい第1の状態遷移列の理想信号と前記再生信号との間の第1メトリックと、当該2値化信号に対応する2番目に確からしい第2の状態遷移列の理想信号と前記再生信号との間の第2メトリックとの差分である差分メトリックを算出する差分メトリック算出部と、
前記差分メトリック算出部によって算出された前記差分メトリックを積算する第1の積算部と、
前記第1の積算部による積算処理回数をカウントする第1のカウント部と、
所定の信号処理閾値以下の前記差分メトリックを抽出する差分メトリック抽出部と、
前記差分メトリック抽出部によって抽出された前記信号処理閾値以下の差分メトリックを積算する第2の積算部と、
前記第2の積算部による積算処理回数をカウントする第2のカウント部と、
前記第1の積算部によって積算された積算値と、前記第1のカウント部によってカウントされたカウント値と、前記第2の積算部によって積算された積算値と、前記第2のカウント部によってカウントされたカウント値とに基づいて予測されるエラーレートを算出するエラーレート算出部と、
前記エラーレート算出部によって算出された前記エラーレートに基づいて標準偏差を算出する標準偏差算出部とを備える再生信号評価装置。 - 前記信号処理閾値は、前記最も確からしい第1の状態遷移列の理想信号と前記2番目に確からしい第2の状態遷移列の理想信号との間のユークリッド距離の2乗値である請求項7記載の再生信号評価装置。
- 前記エラーレート算出部は、前記第1の積算部によって積算された積算値と、前記第1のカウント部によってカウントされたカウント値と、前記第2の積算部によって積算された積算値と、前記第2のカウント部によってカウントされたカウント値とを引数とした一次式を用いて、前記差分メトリック出力の平均値以下の差分メトリックの標準偏差を算出し、その標準偏差から前記エラーレートを算出する請求項7又は8記載の再生信号評価装置。
- 前記一次式は、ニュートン法による反復処理を用いて算出される近似式である請求項9記載の再生信号評価装置。
- 前記エラーレート算出部は、前記第1の積算部によって積算された積算値と前記第1のカウント部によってカウントされたカウント値とに基づいて算出した前記差分メトリックの平均値と、前記第2の積算部によって積算された積算値と前記第2のカウント部によってカウントされたカウント値とに基づく所定の演算結果とに基づいて、前記エラーレートを算出する請求項7~10のいずれかに記載の再生信号評価装置。
- 情報記録媒体から再生された再生信号からPRML信号処理方式を用いて生成された2値化信号に基づいて、当該再生信号の品質を評価する再生信号評価装置であって、
前記2値化信号から、ビットエラーを引き起こす可能性のある複数の状態遷移パターンを抽出するパターン抽出部と、
前記パターン抽出部によって抽出された状態遷移パターン毎に、前記2値化信号に基づいて、当該2値化信号に対応する最も確からしい第1の状態遷移列の理想信号と前記再生信号との間の第1メトリックと、当該2値化信号に対応する2番目に確からしい第2の状態遷移列の理想信号と前記再生信号との間の第2メトリックとの差分である差分メトリックをそれぞれ算出する差分メトリック算出部と、
前記差分メトリック算出部によって算出された前記差分メトリックを前記状態遷移パターン毎に積算する第1の積算部と、
前記第1の積算部による積算処理回数を前記状態遷移パターン毎にカウントする第1のカウント部と、
所定の信号処理閾値以下の前記差分メトリックを前記状態遷移パターン毎に抽出する差分メトリック抽出部と、
前記差分メトリック抽出部によって前記状態遷移パターン毎に抽出された前記信号処理閾値以下の差分メトリックをそれぞれ積算する第2の積算部と、
前記第2の積算部による積算処理回数を前記状態遷移パターン毎にカウントする第2のカウント部と、
前記第1の積算部によって積算された複数の積算値と、前記第1のカウント部によってカウントされた複数のカウント値と、前記第2の積算部によって積算された複数の積算値と、前記第2のカウント部によってカウントされた複数のカウント値とに基づいて予測される複数のエラーレートを前記状態遷移パターン毎に算出するエラーレート算出部と、
前記エラーレート算出部によって算出された前記複数のエラーレートの総和に基づいて標準偏差を算出する標準偏差算出部とを備える再生信号評価装置。 - 情報記録媒体である光ディスクを再生して得られる再生信号からPRML信号処理方式を用いて2値化信号を生成する再生部と、
請求項7~12のいずれかに記載の再生信号評価装置とを備える光ディスク装置。
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IN2010KN02337A (ja) | 2015-05-15 |
TW201042641A (en) | 2010-12-01 |
KR20110124122A (ko) | 2011-11-16 |
CN101952891A (zh) | 2011-01-19 |
TWI457916B (zh) | 2014-10-21 |
MX2010007257A (es) | 2010-11-25 |
RU2505869C2 (ru) | 2014-01-27 |
EP2395510A1 (en) | 2011-12-14 |
US8248902B2 (en) | 2012-08-21 |
CN101952891B (zh) | 2014-07-16 |
BRPI1000035A2 (pt) | 2016-09-27 |
AU2010202774A1 (en) | 2010-08-19 |
US20100214895A1 (en) | 2010-08-26 |
JP5441906B2 (ja) | 2014-03-12 |
CA2707918A1 (en) | 2010-08-03 |
JPWO2010089987A1 (ja) | 2012-08-09 |
EP2395510A4 (en) | 2015-06-17 |
RU2010126600A (ru) | 2013-03-10 |
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