JP3427820B2 - Compression encoding method, recording medium recording compression encoding program, and electronic camera for implementing compression encoding method - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a compression encoding method, a recording medium recording a compression encoding program, and an electronic camera implementing the compression encoding method. In particular, the present invention relates to a technique for appropriately compressing and encoding image data by effectively utilizing the image capturing condition of the image data (the setting condition when capturing the image data or the condition of the capturing environment).

The image pickup conditions are literally the image pickup conditions. Therefore, the image compression conditions such as the target compression rate and the target compression code amount (so-called super fine mode, fine mode, normal mode, etc.) and the actual condition of the image data after image pickup (the high frequency component of the image signal, etc.) Is not included at all.

[0003]

2. Description of the Related Art Generally, in an electronic camera or a computer, in order to efficiently record image data on a recording medium,
Compression coding for image data (for example, DPCM or J
Processing such as PEG compression). Below is a typical JPE
The procedure of G compression is shown in the following (1) to (4).

(1) The image data is divided into pixel blocks of about 8 × 8 pixels. DCT for these pixel blocks
Orthogonal transformation such as transformation (discrete cosine transformation) is performed to transform the image data into spatial frequency components. (2) Prepare a standard quantization table that defines the quantization step for each spatial frequency component of about 8 × 8.
The standard quantization table is multiplied by the scale factor SF to create a quantization table to be actually used. (3) DCT using the quantization table created above
The transform coefficient after the transform is quantized. (4) Encoding such as variable length encoding and run length encoding is performed on the quantized data.

By the way, when the above procedure is performed,
The code amount after compression varies greatly due to individual differences in image data. Therefore, in general JPEG compression, the value of the scale factor is adjusted while performing trial compression a plurality of times so that the final code amount falls within a desired range.

In the present application, adjustable elements that affect the magnitude of the compression code amount in the compression encoding process, such as the scale factor, are collectively referred to as "compression parameters".

[0007]

Generally, in image data, characteristics such as a spatial frequency component and a noise amount change depending on differences in camera settings at the time of image pickup and a shooting environment.
However, in the conventional compression encoding method, the same compression encoding processing is uniformly applied to image data having different imaging conditions.

[0008] Therefore, general image compression encoding cannot be applied to image data captured under special image capturing conditions, and trial compression is repeated many times until the target compression code amount can be compressed. Was likely to occur. Further, since the spatial frequency distribution of noise is peculiar in the image data imaged under the special imaging condition, the noise is likely to be noticeable after decoding, or the image quality deterioration is likely to occur.

In view of the above, it is an object of the present invention to properly compress and code image data by making effective use of the conditions under which the image data was captured during the compression and coding process of the image data. In particular, the inventions set forth in claims 1 and 2 have an object to promptly compression-encode the image data to a desired compression code amount by effectively utilizing the condition when the image data was captured. It is an object of the inventions according to claims 3 to 5 to specifically show variations of imaging conditions effective in optimizing the compression coding method. It is an object of the invention according to claim 6 to provide a recording medium recording a compression encoding program for realizing the compression encoding method according to any one of claims 1 to 5 on a computer. To do. It is an object of the invention described in claim 7 to provide an electronic camera for carrying out the compression encoding method according to any one of claims 1 to 5 .

[0010]

[Means for Solving the Problems] Means for solving the problems will be described below by associating step numbers and symbols in the embodiments described later. The correspondence here is for reference only and does not limit the content of the present invention.

[0011]

[0012]

[0013] The invention described in (Claim 1) according to claim 1, using the compression parameters for trial, trial step of compressing and encoding image data (S
16, S17) and the compression result of the image data in the trial step are applied to the “statistical relationship between the compression parameter and the compression code amount” obtained by performing test compression encoding of a plurality of image data in advance, Parameter estimation step (S18 to S22) for estimating a compression parameter for compressing the image data to a target compression code amount, and a compression step (S23) for compression encoding the image data using the compression parameter estimated in the parameter estimation step. To S26), the parameter estimation step prepares the statistical relationship obtained for each variation of the imaging condition in association with the imaging condition and prepares the imaging condition of the image data. Then, the statistical relationship is selectively used. However, the imaging conditions are
It is a setting condition or a photographing environment condition when the image data is picked up, and causes a significant difference in the image data, and the difference affects the compression code amount.
The image capturing conditions are different from the image compression conditions such as the target compression rate and the target compression code amount, and the actual condition of the image data after the image capturing (such as the high frequency component of the image signal). Further, the compression parameter is an adjustable element that affects the compression code amount in the process of the compression coding process.

In the parameter estimation step of claim 1 ,
A statistical relationship is prepared for each variation of imaging conditions. In such a statistical relationship, the population is limited in advance by the imaging conditions, and therefore statistical data variation is small and reliability is extremely high. Further, in the above parameter estimation step, these highly reliable statistical relationships are used properly according to the imaging conditions of the compression target. Therefore, the compression parameter is estimated based on an accurate and appropriate statistical relationship, and the accuracy of parameter estimation is further improved.

(Invention 2 ) In the invention described in Claim 2 , a trial step (S) of compressing and coding image data using a trial compression parameter.
37, S38), a parameter estimation step (S39, S40) for estimating a compression parameter for compressing the image data to a target compression code amount based on the compression result of the image data in the trial step, and a parameter estimation step. In the compression encoding method having a compression step (S41 to 43) of compressing and encoding image data using the estimated compression parameter, the compression step includes "the compression parameter obtained in the parameter estimation step" and "the target. The correction process obtained from the relationship between the correct value of the compression parameter for compressing to the compression code amount is prepared according to the variation of the imaging condition, and the correction process corresponding to the imaging condition of the image data is used. And compressing the compression parameter estimated in the parameter estimation step. Goka way. However, the imaging condition is a setting condition or a photographing environment condition when the image data is imaged, and causes a significant difference in the image data, and the difference affects the compression code amount. The image capturing conditions are different from the image compression conditions such as the target compression rate and the target compression code amount, and the actual condition of the image data after the image capturing (such as the high frequency component of the image signal). further,
The compression parameter is an adjustable element that affects the compression code amount in the compression encoding process.

In the compression step of claim 2 , the compression parameter obtained in the parameter estimation step is corrected. Such a correction process can be obtained, for example, by statistically analyzing the relationship between the “compression parameter obtained in the parameter estimation step” and the “correct value of the compression parameter obtained experimentally”. Normally, the content of this correction process also changes depending on the imaging condition of the image data. Therefore, in the compression step described above, a correction process is prepared for each variation of the image pickup conditions, and the correction process is selectively used according to the image pickup condition of the image data. As a result, it is possible to flexibly correspond to the characteristics of the image data for each imaging condition and to accurately correct the compression parameter.

[0017]

[0018]

[0019]

[0020]

( Claim 3 ) The invention according to claim 3 is the compression coding method according to any one of claims 1 and 2 , wherein the image pickup condition is that the image pickup section picks up the image data. Conditions, such as imaging sensitivity setting, signal gain, gamma correction curve, electronic zoom presence / absence, electronic zoom magnification, shutter speed, white balance adjustment value, special shooting effect, gradation, edge enhancement, monochrome mode, exposure correction value , A noise reduction mode, a wide dynamic range mode, and the number of output pixels.

( Claim 4 ) According to a fourth aspect of the present invention, in the compression encoding method according to any one of the first and second aspects, the photographing condition is that the image data is photographed. Whether or not strobe is used and whether slow sync is used, which are environmental conditions,
It is characterized by at least one of presence / absence of daytime synchronization, photometric value, multi-pattern photometric value, light distribution state of subject, presence / absence of vertical position photographing, camera shake amount, temperature, and photometric mode.

( Claim 5 ) According to a fifth aspect of the present invention, in the compression encoding method according to any one of the first and second aspects, the image pickup condition is the image pickup of the image data. The conditions of the lens, macro shooting, image magnification, depth of field, aperture value, focal length, shooting angle of view, subject distance, focusing status, multi-point focusing status, shooting lens type, converter lens At least one of presence / absence, type of converter lens, presence / absence of optical filter, and type of optical filter.

[0024]

( Claim 6 ) The recording medium according to claim 6 includes any one of claims 1 to
A compression encoding program for causing a computer to execute the compression encoding method according to any one of 5 above is recorded.

( Claim 7 ) An electronic camera (10) according to a seventh aspect of the invention is an image pickup section (11, 13, 15,) for picking up an image of a subject and generating image data.
16 and 17) and a compression processing unit (18) for performing the compression encoding method according to any one of claims 1 to 5 on the image data generated by the imaging unit. It is characterized by

[0027]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings.

<First Embodiment> The first embodiment is an embodiment of an electronic camera corresponding to the invention described in claims 1, 3, and 7 . FIG. 1 is a block diagram showing the configuration of the electronic camera 10. In FIG.
The electronic camera 10 is equipped with a taking lens 11 and a strobe light emitting section 12. An image sensor 13 is arranged in the image space of the taking lens 11.

The image data generated by the image pickup device 13 is processed through the signal processing unit 15, the A / D conversion unit 16 and the image processing unit 17 in this order, and then processed by the compression processing unit 18 as digital image data. Given. Compression processing unit 1
8 compression-encodes this image data and outputs it to the recording unit 19. The recording unit 19 records the compressed image data on a recording medium (not shown) such as a memory card.

Further, the electronic camera 10 includes a control unit 21 including a microprocessor, a multi-photometry unit 22 for performing multi-pattern photometry, a focus detection unit (or a distance measurement unit for performing distance measurement) 23 for focus detection, and a camera operation. A group of operation buttons 24 for setting the mode and the mode are provided. Control unit 21
Is a multi-photometry unit 22, a focus detection unit (or a distance measurement unit) 2
3, the detection information is acquired from the operation button group 24 and the like. The control unit 21 determines the imaging condition (for example, imaging sensitivity setting) of the image data based on the detection information.

The control unit 21 includes the above-mentioned taking lens 11,
A strobe light emitting unit 12, an image pickup device 13, a signal processing unit 15,
The A / D conversion unit 16 and the image processing unit 17 are controlled to execute the image pickup operation that matches the image pickup condition. On the other hand, the compression processing unit 18 acquires this imaging condition from the control unit 21. The compression processing unit 18 uses this imaging condition as effective information for performing proper compression encoding. Hereinafter, the operation of the compression processing unit 18, which is a feature of the present invention, will be described in detail.

(Preparation for compression encoding) FIG. 2 is a flow chart showing a procedure for preparation for compression encoding. Such preparation is usually performed by the developer of the compression processing unit 18. Of course, the user of the electronic camera 10 may select the image data having a high shooting frequency and execute the preparation.

The preparatory procedure will be described with reference to FIG. Here, for convenience of explanation, the executor of the preparation is assumed to be the developer. First, the developer is the electronic camera 1
While changing the imaging sensitivity setting of 0, as many kinds of subjects and scenes as possible are photographed. The developer performs DCT conversion on the uncompressed image data (hereinafter referred to as “test image”) collected in this way (S11 in FIG. 2).

Next, the developer repeatedly executes the quantization and the coding on each test image after the DCT conversion while gradually changing the value of the scale factor SF, and the (scale factor SF, the compression code amount). A large number of ACVdata) data is obtained (S12 in FIG. 2). FIG. 4 is a graph obtained by plotting these data obtained for a test image having an imaging sensitivity of ISO200. Further, FIG. 5 is a graph in which the data obtained for the test image with the imaging sensitivity ISO1600 is plotted.

As shown in FIGS. 4 and 5, a clear difference appears in the data distribution on the graph due to the difference in imaging sensitivity. It is considered that such a difference in data distribution is due to a difference in noise amount depending on the imaging sensitivity setting. Here, the developer selects scale factors SF that are considered to be standard in achieving the target compression rates of 1/4, 1/8, and 1/16 from the graphs of FIGS. 4 and 5, respectively (see FIGS. 4 and 5). 5 and the initial scale factor ISF.

FIG. 3 shows the initial scale factors ISF thus selected, arranged in a data table.
The developer uses such a data table as the compression processing unit 1
8 is stored in a rewritable memory area (FIG. 2S1).
3). Next, the developer performs regression analysis on the data shown in FIG. 4 and FIG. 5, and determines undetermined coefficients a and b that apply to log (ACVdata) = a · log (SF) + b [1] for each test image. Ask (S14 in FIG. 2). Note that the regression analysis here is performed by limiting the scale factor range to 0.1 to 1.0 in order to further improve the degree of agreement between the regression equation and the data.

FIG. 6 is a plot of the undetermined coefficients a and b thus obtained, where the horizontal axis is a and the vertical axis is b. As can be seen from FIG. 6, the distribution of the undetermined coefficients a and b is divided into two depending on the difference in the imaging sensitivity setting.
Here, the developer performs regression analysis of undetermined coefficients a and b separately for each imaging sensitivity setting, and in the case of ISO200: b = C1 _ISO200 · a + C2 _ISO200 ... [2] In the case of ISO1600: b = C1 _ISO1600・ a + C2 _ISO1600 ... Coefficients applicable to [3] C1 _ISO200 , C2 _ISO200 , C1
Obtain _ISO1600 and C2 _ISO1600 respectively. The developer stores these coefficients in a rewritable memory area in the compression processing unit 18 in a state of being associated with the imaging sensitivity setting (S15 in FIG. 2). With the above procedure, the preparation for compression encoding is completed.

(Explanation of Compression Encoding Method) Next, a specific compression encoding procedure will be described. FIG. 7 is a flow chart illustrating a compression encoding method executed by the compression processing unit 18. First, the compression processing unit 18 acquires the imaging condition (here, the imaging sensitivity setting) of the image data from the control unit 21. The compression processing unit 18 searches the data table (FIG. 3) created in the preparation based on the imaging sensitivity setting and the target compression ratio, and determines the initial scale factor ISF (corresponding to the trial compression parameter). (FIG. 7 S16).

The compression processing unit 18 multiplies the standard quantization table by the thus determined initial scale factor ISF to create an initial quantization table used for trial compression. The compression processing unit 18 executes a known JPEG compression procedure using this initial quantization table, and executes trial compression of image data (S17 in FIG. 7). Next, the compression processing unit 18
Is the coefficient C1 _ISO200 , C2 _ISO200 , C prepared in the preparation.
Among 1 _{ISO 1600,} C2 _{ISO 1600,} singled out those that match the imaging sensitivity setting of the image data, a coefficient C1, C2 (FIG 7S18, S19, S20).

Next, the compression processing section 18 uses the coefficients C1,
Substituting C2, the code amount ACVdata after trial compression, and the initial scale factor ISF into the following equation, a = {log (ACVdata) -C2} / {log (ISF) + C1} ... [4] is calculated. Then, the undetermined coefficient a is determined (S21 in FIG. 7). [Equation 4] is an equation derived from [Equations 1 to 3] and represents a statistical relationship between the scale factor and the compression code amount.

Next, the compression processing section 18 uses the target compression code amount TCV (= code amount of image data × target compression rate) to obtain NSF = (ACVdata / TCV) ^{(-1 / a)} .ISF.multidot. .. [5] is calculated and an appropriate target scale factor NSF is obtained in order to obtain the target compression code amount TCV (S22 in FIG. 7).

[Equation 5] is an equation in which (target scale factor NSF, target compression code amount TCV) is substituted into [Equations 1 to 4] for rearrangement and the undetermined coefficient b is erased. Subsequently, the compression processing unit 18 determines the target scale factor NS
A standard quantization table is multiplied by F to create a quantization table (S23 in FIG. 7). The compression processing unit 18 uses this quantization table to perform a known JPEG compression procedure (S24 to S24).
26) is executed to compress the image data.

Here, the compression processing section 18 determines whether or not the code amount after image compression falls within the allowable range of the target compression code amount TCV (S27 in FIG. 7). If it is out of the allowable range (NO side in S27 of FIG. 7), the compression processing unit 18
Returns the operation to step S22, updates the target scale factor NSF, and repeats image compression again. On the other hand, if it is within the allowable range (YES side in S27 of FIG. 7), the compression processing unit 18 determines that the desired image compression has been achieved, and ends the operation.

(Effect of First Embodiment) As described above, in the first embodiment, the initial scale factor that is as close to the correct answer as possible is selected based on the information of the imaging sensitivity setting. Therefore, it is possible to efficiently reduce the number of trial compressions until the target compression code amount is reached.
Further, since the initial scale factor is close to the correct answer, the result of the trial compression accurately reflects the relationship between the scale factor and the compression code amount near the correct answer. Therefore, the target scale factor can be estimated more accurately.

Moreover, in the first embodiment, the statistical relationship (coefficients C1 and C2 defining the statistical relationship) is prepared for each imaging sensitivity setting. Therefore, the reliability of each statistical relationship is sufficiently high. Therefore, also from this point, it is possible to more accurately estimate the target scale factor.

By the way, FIG. 8 plots the relationship between the target scale factor obtained by one trial compression and the accurate scale factor (actual measurement value) for obtaining the compression rate of 1/4 for many image data. It is a graph. In addition,
8 indicates that the target scale factor is calculated without dividing the imaging sensitivity setting, and the black triangle indicates that the target scale factor is calculated by dividing the imaging sensitivity setting. As clearly shown in FIG. 8, when the imaging sensitivity setting is divided (black triangle), it is closer to the correct answer line (dotted line in FIG. 8), that is, a more accurate target scale factor. I understand. Next, another embodiment will be described.

<Second Embodiment> The second embodiment is an embodiment of an electronic camera corresponding to the invention described in claims 2 , 4, and 7 . The configuration of the electronic camera according to the second embodiment is the same as that of the first embodiment (FIG. 1), and thus the description thereof is omitted here. Hereinafter, the operation of the compression processing unit 18, which is a feature of the second embodiment, will be described.

(Preparation for compression encoding) FIG. 9 is a flow chart showing a procedure for preparation for compression encoding in the second embodiment. The preparatory procedure will be described with reference to FIG. Here, for convenience of explanation, the executor of the preparation is assumed to be the developer. First, the developer shoots as many kinds of subjects and scenes with the electronic camera 10 as possible while switching whether or not the strobe is used. The developer executes DCT conversion on the uncompressed image data (hereinafter referred to as “test image”) collected in this way (S3 in FIG. 9).
1).

Next, the developer repeatedly executes the quantization and the coding on each test image after the DCT transform while gradually changing the value of the scale factor SF, and then (scale factor SF, compression code amount). ACVdata) is obtained in large numbers (S32 in FIG. 9). FIG. 11 is a graph in which data of (scale factor SF, compression code amount ACVdata) is plotted for a test image using a strobe. On the other hand, FIG. 4 shows (scale factor SF, compression code amount AC for a test image that does not use strobe).
(Vdata) is a graph obtained by plotting data.

As shown in FIGS. 4 and 11, the data distribution on the graph varies depending on whether or not the strobe is used. This is when using a strobe
It is considered that this is because the background portion is blackened and the amount of image information is reduced. Here, the developer selects scale factors SF that are considered to be standard in achieving the target compression ratios of 1/4, 1/8, and 1/16 from the graphs of FIGS. Positions of white circles shown in FIG. 11) and initial scale factor ISF.

FIG. 12 shows the initial scale factors ISF thus selected arranged in a data table.
The developer uses such a data table as the compression processing unit 1
8 is stored in a rewritable memory area (S3 in FIG. 9).
3). Next, the developer performs regression analysis on the data shown in FIG. 4, and obtains undetermined coefficients a and b that apply to log (ACVdata) = a · log (SF) + b ... [1] (S34 in FIG. 9).

The developer has determined the undetermined coefficient a,
Regression analysis is performed for b, and coefficients C1 and C2 that apply to b = C1 · a + C2 ... [6] are obtained. The developer stores these coefficients in a rewritable memory area in the compression processing unit 18 (S35 in FIG. 9). Next, the developer uses the coefficients C1 and C2 to estimate the target scale factor for the test image when the strobe is used.

The ⋄ mark in FIG. 13 is a plot of the target scale factor (before correction) estimated in this way. In this case, the target scale factor before correction (◇
The mark) is distributed at a position slightly deviated from the correct line (dotted line in FIG. 13). Therefore, the developer performs a regression analysis on the target scale factor before correction (marked with ⋄), and obtains a correction formula for correcting the regression line formula of "target scale factor before correction" into the formula of the correct answer line. . The developer
This correction formula is stored in the rewritable memory area in the compression processing unit 18 (S36 in FIG. 9). By the above procedure,
The preparation for compression encoding is completed.

(Explanation of Compression Encoding Method) Next, a specific compression encoding method will be described. FIG. 10 is a flow chart illustrating a compression encoding method executed by the compression processing unit 18. First, the compression processing unit 18 acquires the imaging condition of image data (whether or not a strobe is used here) from the control unit 21. The compression processing unit 18 searches the data table (FIG. 12) created in the preparation based on the use / non-use of the strobe and the target compression ratio, and determines the initial scale factor ISF (S37 in FIG. 10).

The compression processing unit 18 multiplies the standard quantization table by the thus determined initial scale factor ISF to create an initial quantization table used for trial compression. The compression processing unit 18 executes a known JPEG compression procedure using this initial quantization table, and executes trial compression of image data. (FIG. 10S38). Next, the compression processing unit 18 substitutes the coefficients C1 and C2 prepared in advance, the compression code amount ACVdata after this trial compression, and the initial scale factor ISF into the following equation to obtain a = {log (ACVdata ) -C2} / {log (ISF) + C1} ... [7] is calculated and the undetermined coefficient a is determined (FIG. 10 S39).

Next, the compression processing unit 18 uses the target compression code amount TCV (= code amount of image data × target compression ratio): NSF = (ACVdata / TCV) ^{(-1 / a)} .ISF. .. [5] is calculated and an appropriate target scale factor NSF for obtaining the target compression code amount TCV is obtained (S40 in FIG. 10).

Here, the compression processing section 18 determines whether or not the image data was captured in the state of using the strobe (S41 in FIG. 10). In the case of the image data captured without using the strobe, the compression processing unit 18 determines that the correction processing is not necessary, and moves the operation to step S43. on the other hand,
In the case of image data captured in the state of using the strobe, the compression processing unit 18 corrects the target scale factor NSF using the correction formula obtained in the preparation (FIG. 10S42),
The operation moves to step S43.

Next, the compression processing section 18 re-image-compresses the image data using the target scale factor NSF (S43 in FIG. 10). Here, the compression processing unit 18 determines whether or not the code amount after image compression falls within the allowable range of the target compression code amount TCV (S44 in FIG. 10). If it is out of the allowable range (NO side in S44 of FIG. 10), the compression processing unit 18 returns the operation to step S40, updates the target scale factor NSF, and repeats the image compression again. On the other hand, if it is within the allowable range (Y in FIG. 10S44).
The ES side), the compression processing unit 18 determines that the desired image compression has been achieved, and ends the operation.

(Effect of Second Embodiment) As described above, in the second embodiment, an initial scale factor close to the correct answer is selected according to whether or not the strobe is used. Therefore, the number of trial compressions until the target compression code amount is reached can be efficiently reduced. Further, since the initial scale factor is close to the correct answer, the result of the trial compression accurately reflects the relationship between the scale factor and the compression code amount near the correct answer. Therefore, it is possible to more accurately estimate the target scale factor.

Further, in the second embodiment, since the target scale factor is corrected for the image data using strobe, the target scale factor can be obtained more accurately. Incidentally, the black triangle mark in FIG. 13 is a plot of the corrected target scale factor.

As shown in FIG. 13, the corrected target scale factor (black triangle) is closer to the correct line (dotted line in FIG. 8) than the uncorrected target scale factor (⋄). That is, it can be seen that the target scale factor is accurately corrected. Next, another embodiment will be described.

<Third Embodiment> A third embodiment is an embodiment of an electronic camera corresponding to the invention described in claims 3 and 7 . The configuration of the electronic camera according to the third embodiment is the same as that of the first embodiment (FIG. 1), and thus the description thereof is omitted here. Less than,
The operation of the compression processing unit 18, which is a feature of the third embodiment, will be described.

(Preparation for compression encoding) FIG. 14 is a flow chart showing a procedure for preparation for compression encoding in the third embodiment. A feature of the preparation in the third embodiment is that the standard quantization table is used properly according to the imaging sensitivity setting of the test image (S12a in FIG. 14).

FIG. 15A shows a standard quantization table dedicated to ISO200 used at this time. Also, IS
The standard quantization table dedicated to O1600 is shown in FIG.
Shown in. These standard quantization tables are determined for each imaging condition by the image quality evaluation of the decoded image and the like. Other operations shown in FIG. 14 (S11, S1
3 to S15) are the same as those in the first embodiment (FIG. 2), and thus the description thereof is omitted here.

(Explanation of compression encoding method) FIG.
3 is a flowchart showing a compression encoding method in the embodiment of FIG. The characteristic features of the operation in the third embodiment are the following (1) and (2).

(1) The compression processing section 18 selects the standard quantization table according to the image pickup sensitivity setting of the image data (S16a in FIG. 16).

(2) The compression processing section 18 executes trial compression (FIG. 16S17) and main compression (FIGS. 16S23 to 26) using the selected standard quantization table. In addition,
Other operations shown in FIG. 16 are the same as those in the first embodiment (FIG. 7), and thus the description thereof is omitted here.

(Effect of Third Embodiment) The ISO described above
In the standard quantization table dedicated to 1600, the quantization coefficient of the high spatial frequency component is set to be large. Therefore,
It is possible to effectively suppress a high frequency noise component that tends to occur in a high imaging sensitivity setting and prevent the compression code amount from meaninglessly increasing due to noise. On the other hand, IS
In the standard quantization table dedicated to O200, the quantization coefficient of the high spatial frequency component is set to be relatively small. Therefore, it is possible to suppress the disappearance of high-frequency minute signals and mosquito noise and prevent image quality deterioration as much as possible.

<Supplementary Items of Embodiment> In the above embodiment, the case where the present invention is applied to an electronic camera has been described. In this case, there is a structural advantage that the imaging condition of the image data can be directly acquired from the electronic camera. However, the embodiments of the present invention are not limited to electronic cameras.

For example, the present invention may be applied to a scanner device or the like. In this case, the image capturing conditions of the scanner device (for example, scan speed, scan method, scan size, scan object type, illumination type, etc.) are effectively used to properly compress and encode the scanned image data. It becomes possible. Furthermore, the operation procedure of FIG. 7, FIG. 10 or FIG. 16 may be recorded on a recording medium (corresponding to claim 6) as a compression encoding program. in this case,
The compression encoding method of the present invention can be executed on a computer.

Further, in the above-mentioned embodiment, the case where the JPEG method is adopted as the image compression method has been described, but the present invention is not limited to this. For example,
As the image compression method, the MPEG method, the DPCM method, or the like may be adopted. Of course, the present invention may be applied to compression of moving images. Furthermore, in the above-described embodiment, the case where the scale factor is used as the compression parameter has been described, but the present invention is not limited to this. In general,
Any adjustable element that affects the compression code amount in the compression encoding process can be used as the compression parameter.

For example, it is possible to change the compression code amount even if the compression distribution in the spatial frequency domain is changed by changing each quantization coefficient in the quantization table. Therefore, the compression distribution in the spatial frequency domain (for example, each quantization coefficient in the quantization table) may be used as the compression parameter. Further, in the above-described embodiment, the procedure of estimating the scale factor by at least one trial compression has been described. In this case, there is an advantage that the estimation accuracy of the target scale factor can be increased to a very high level by at least one trial compression by effectively using the information of the imaging condition. However, the present invention is not limited to this, and the present invention can be applied to a known compression parameter estimation procedure in which trial compression is repeated a plurality of times.

In the above-described embodiment, "imaging sensitivity setting" or "presence or absence of strobe use" is set as the imaging condition.
The case of using is explained. In particular, when such an imaging sensitivity setting is used as an imaging condition, there is an advantage that appropriate compression encoding can be executed while being sensitive to changes in the amount of noise. However, the imaging condition of the present invention is not limited to this. In general, the imaging condition may be any as long as it causes a significant (for example, statistical) difference in image data and the difference affects the compression result (compression code amount, decoded image quality, etc.). . Under such imaging conditions, the effect of the present invention can be obtained. For example, such an imaging condition includes an imaging unit condition, an imaging environment condition, an imaging lens condition, and the like.

Further, in the above-mentioned embodiment, the compression encoding is controlled according to one kind of image pickup condition, but the invention is not limited to this. For example, compression encoding may be controlled according to a plurality of types of imaging conditions. in this case,
By logically combining a plurality of types of imaging conditions, it is possible to make a dramatically fine case classification, and it is possible to finely control the compression encoding processing.

In the above embodiment, the image pickup condition is determined based on various settings of the image pickup mode (not including the image compression mode such as the super fine mode). It is not limited to. For example, the user may judge the imaging condition and input information via the operation button or the like. In this case, according to the method of the present invention, it is possible to acquire the imaging conditions such as the light distribution state of the subject in more detail and perform more appropriate compression encoding. In addition, the compression processing unit or the like in the electronic camera can change the image capturing condition from the microprocessor, the image capturing unit, the signal processing unit, the photometric unit, the focus detection unit (distance measuring unit), the image processing unit, the flash light emitting unit, the photographing lens, or the like. The information may be acquired. Hereinafter, specific imaging conditions listed in the inventions of claims 3 to 5 will be described.

[A] Imaging sensitivity setting, signal gain, gamma correction curve As the imaging sensitivity is manually or automatically set to high sensitivity (as the signal gain of the imaging unit is increased), night and shade can be imaged brightly, but The noise level also increases. Further, as the γ of the gamma correction curve is increased, the minute amplitude gain of the image pickup unit is increased, and the noise level of the screen dark part is increased. Under such an imaging condition that the noise level increases, the compression code amount increases by the increase in noise, so the following compression encoding is preferable. ● In the trial step, set the compression parameter for trial to have a high degree of compression. ● In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data with a high noise level. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data having a large noise level. ● Change the standard quantization table or the quantization coefficient to strongly suppress the spatial frequency component of noise.

[B] Presence or absence of electronic zoom, magnification of electronic zoom As the electronic zoom is used or the magnification of the electronic zoom is increased, the substantial resolution of the image data is lowered. In this case, the high spatial frequency component of the image data is lost, and the compression code amount is inevitably reduced. Therefore,
In the case of an image pickup condition using electronic zoom, the following compression coding is preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data of substantially low resolution as described above. In the compression step, the compression parameter is corrected by using the correction process obtained for the image data of substantially low resolution as described above. ● Change the standard quantization table or the quantization coefficient to increase the quantization coefficient for the missing high spatial frequency components.

[C] Shutter Speed As the shutter speed becomes slower, image blur due to camera shake or subject shake easily occurs. In this case, the high spatial frequency component is missing from the signal component, and the compression code amount is relatively small. Therefore, the following compression encoding is preferable under the imaging condition of the low-speed shutter in which the loss of the high spatial frequency component is remarkable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data of the low speed shutter. ● In the compression step, the compression parameter is corrected using the correction processing obtained for the image data of the low-speed shutter. ● Based on subjective evaluation of image quality, determine standard quantization table or quantization coefficient distribution suitable for low-speed shutter image data. This standardized quantization table or distribution of quantization coefficients is used according to the imaging condition of the low-speed shutter.

On the other hand, the noise component increases as the shutter speed becomes slower and the CCD accumulation time becomes longer (especially, noise in the high spatial frequency component increases). When such a change in the amount of noise cannot be ignored, a characteristic in which these actions are overlapped appears statistically for each shutter speed. (For example, in a high-speed shutter of 1/100 second or less, a change of a signal component due to image flow does not occur so much, and a change of a noise component appears largely. The change in the component becomes noticeable, and the change in the noise component can be ignored.) The compression encoding may be performed in accordance with such characteristics of the image data for each shutter speed. At this time, it is preferable to perform compression encoding in consideration of whether or not a tripod is used.

[D] White balance adjustment value Depending on the white balance adjustment value of the image pickup unit, such as outdoor shooting / indoor shooting, sunny shooting / cloudy shooting, sunset shooting / daytime shooting, shooting location, shooting time, hue, It is possible to roughly divide the saturation into groups. By grouping the image data based on the white balance adjustment value in this way, the following compression encoding is possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[E] Special shooting effect Based on the type of special shooting effect (monochrome processing, embossing effect, bright and dark blur effect, high key processing, low key processing, chroma key processing, noise addition effect, mosaic effect, etc.), the characteristics of image data Can be roughly divided into groups. In this way, by grouping the image data for each type of special photographing effect, the following compression encoding becomes possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[F] Gradation The contrast, noise amount, detail, hue, saturation, etc. of the image data change depending on the degree of gradation correction. Therefore, the characteristics of the image data can be roughly divided into groups based on the gradation imaging conditions. In this way, by grouping the image data according to the gradation imaging conditions, the following compression coding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[G] Use of strobe When the strobe is used, the background to which the strobe light does not reach is crushed in black, and the brightness level is likely to be lost in a partial area of the screen. When such a lack of brightness level occurs, the amount of information of image data decreases and the amount of compression code of image data decreases. Therefore, in the case of an imaging condition using a strobe, the following compression coding is generally preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. ● In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data using strobe. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data using strobe light. ● Determine the standard quantization table or distribution of quantization coefficients that is suitable for strobe image data based on subjective evaluation of image quality. For image data using strobe, this standard quantization table or distribution of quantization coefficients is used.

[H] Use of Slow Synchro If slow synch is used, the background is photographed brightly. Therefore, the brightness level is less likely to be crushed as compared with the case of simply using a strobe. Therefore, in the case of the slow synchronization shooting condition, it is preferable to perform the compression coding in distinction from the simple flash shooting condition.

[I] Use of daytime synchronization When daytime synchronization is used, both the background and the subject are photographed brightly. Therefore, unlike the mere use of a strobe, the brightness level is hardly collapsed. Therefore, in the case of the shooting condition of daytime synchronization, it is preferable to perform the compression encoding in distinction from the shooting condition of the simple stroboscopic shooting.

[J] Photometric value Based on the photometric value, the characteristics of the image data can be roughly grouped. In this way, by grouping the image data according to the photometric value, the following compression coding is possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[K] Multi-pattern photometric value Based on the multi-pattern photometric value, the light distribution states (backlight, forward light, etc.) of the subject can be divided into groups.
In this way, by grouping the image data according to the multi-pattern photometric value, the following compression coding is possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[L] Light distribution state of subject Light distribution state of subject (for example, backlight, forward light, side light, oblique light,
The image data can be roughly grouped based on (semi-backlit). As described above, by grouping the image data according to the light distribution state, the following compression encoding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[M] The screen configuration of the image data can be roughly divided into groups depending on whether the vertical position shooting is performed or not. In this way, by grouping the image data according to whether or not the vertical position shooting is performed, the following compression encoding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[N] Camera shake amount As the camera shake amount increases, the image becomes easier to flow. In this case, the high spatial frequency component of the image data is lost, and the compression code amount is inevitably reduced. Therefore, under such an imaging condition that the camera shake amount is large, the following compression coding is preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. ● In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data having a large camera shake amount. ● In the compression step, the compression parameter is corrected using the correction processing obtained for the image data having a large camera shake amount. ● Determine the standard quantization table or distribution of quantization coefficients suitable for image data with large camera shake based on subjective evaluation of image quality. This standardized quantization table or the distribution of quantization coefficients is used according to the imaging condition with a large camera shake amount.

[O] Presence / absence of macro photography, image magnification fractal object having no characteristic of fractal figure (artificial object, etc.)
On the other hand, when high magnification shooting such as macro shooting is performed,
The actual resolution of the image data is low. In this case, the high spatial frequency component of the image data is lost, and the compression code amount becomes relatively small. Therefore, in high-magnification photography such as macro photography, the following compression coding is preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. ● In the parameter estimation step, using the statistical relationship obtained for low-resolution image data as described above,
Estimate compression parameters. In the compression step, the compression parameter is corrected using the correction processing obtained for the low resolution image data as described above. ● Change the standard quantization table or the distribution of quantization coefficients to increase the quantization coefficient for the missing high spatial frequency components.

[P] Depth of field As the depth of field becomes shallower, the amount of blurring increases before and after the subject. In this case, the high spatial frequency component of the image data is lost, and the compression code amount becomes relatively small. Therefore, the following compression coding is preferable under an imaging condition with a shallow depth of field. ● In the trial step, the compression parameter for trial is set to a low degree of compression. ● In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data having a shallow depth of field. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data having a shallow depth of field. ● Based on subjective evaluation of image quality, determine a standard quantization table or distribution of quantization coefficients suitable for image data with a shallow depth of field. Corresponding to imaging conditions with shallow depth of field,
This standardized quantization table or distribution of quantized coefficients is used.

[Q] Aperture value, focal length, shooting angle of view, subject distance Under the following image pickup conditions, the background portion in the screen is likely to be blurred.・ Aperture value is on the release side. -The focal length of the shooting lens is long (the shooting angle of view is narrow). -The subject distance is short. When the image is blurred as described above, the high spatial frequency component of the image data is lost, and the compression code amount of the image data becomes relatively small. Therefore, in the case of such an imaging condition that the blur becomes large, the following compression encoding is preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. ● In the parameter estimation step, an appropriate compression parameter is estimated using the statistical relationship obtained for image data with large blur. ● In the compression step, the compression parameter is corrected by using the correction processing obtained for the image data with large blur. ● Determine the standard quantization table or distribution of quantization coefficients suitable for image data with large blur based on subjective evaluation of image quality. This standardized quantization table or the distribution of quantization coefficients is used according to the imaging condition with large blur.

[R] Focusing state The blurring degree of image data can be divided into groups based on the focusing state obtained from the focus detection unit or the like. By grouping the image data according to the focus state in this way, the following compression encoding is possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[S] Multi-Point Focusing Condition Based on the multi-point focusing condition obtained from the multi-point focus detection unit or the like, it is possible to roughly divide the blur area or blur position in the screen into groups. By grouping the image data according to the multi-point focus state in this way, the following compression encoding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[T] Shooting Lens Type Based on the shooting lens type data, the aberration performance, spatial frequency characteristic (MTF characteristic), blur, etc. of image data can be roughly grouped. By grouping the image data according to the type of the photographing lens in this way, the following compression encoding is possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[U] Temperature As the temperature of the image sensor such as CCD rises, the noise level of the image data increases. Therefore, under high temperature imaging conditions, the compression code amount increases by the amount of noise increase, and therefore the following compression coding is preferable. ● In the trial step, set the compression parameter for trial to have a high degree of compression. ● In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data with a high noise level. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data having a large noise level. ● Change the standard quantization table or the quantization coefficient to strongly suppress the spatial frequency component of noise. Regarding the temperature detection, it is preferable to detect the temperature using a temperature center built in the image pickup device or arranged in the vicinity thereof.

[V] Presence / Absence of Edge Enhancement, Strength of Edge Enhancement In the image processing unit and the image processing program in the electronic camera,
Edge enhancement (sharpness adjustment) is performed to increase the image clarity. By such edge enhancement, the high spatial frequency component of the image data increases and the compression code amount increases. Also, as the degree of edge emphasis is increased,
The compression code amount also becomes large. Therefore, the following compression encoding is preferable under the imaging condition for executing the edge enhancement or the imaging condition for the stronger edge enhancement. ● In the trial step, set the compression parameter for trial to have a high degree of compression. ● Image data with many high spatial frequency components in the parameter estimation step (more specifically, image data with edge enhancement, image data with strong edge enhancement)
The compression parameter is estimated using the statistical relationship obtained for. ● In the compression step, the compression parameters are calculated using the correction processing obtained for the image data with a high spatial frequency component (more specifically, the image data with edge emphasis and the image data with strong edge emphasis). to correct. ● A standard quantization table suitable for image data with more high spatial frequency components (more specifically, image data with edge enhancement or image data with strong edge enhancement) based on subjective evaluation of image quality, or Determine the distribution of quantized coefficients. The standardized quantization table or the distribution of the quantization coefficients is used in correspondence with the imaging condition for executing the edge enhancement or the imaging condition for the stronger edge enhancement.

[W] Whether monochrome mode or not In the image processing unit or image processing program in the electronic camera,
A process of converting color image data into monochrome is performed by user selection or the like. In such monochrome image data, image information relating to color is missing, and therefore the compression code amount is reduced accordingly. Therefore, the compression coding as described below is preferable under the imaging conditions in the monochrome mode. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the monochrome image data. ● In the compression step, the compression parameters are corrected using the correction processing obtained for monochrome image data. ● Determine the standard quantization table or quantization coefficient distribution suitable for monochrome image data based on subjective evaluation of image quality. This standardized quantization table or the distribution of quantization coefficients is used according to the imaging conditions of the monochrome mode.

[X] Exposure correction value (presence / absence of exposure correction, ± direction of exposure correction, correction width of exposure correction, etc.) In an electronic camera, exposure correction is performed at the time of image pickup by user selection or the like. The patterns and tendencies of the captured image data can be grouped based on the exposure correction information (exposure correction value, presence / absence of exposure correction, ± direction of exposure correction, correction width of exposure correction, etc.) during image pickup. .. By such grouping, the following compression coding becomes possible. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[Y] Noise reduction mode In the image processing unit and the image processing program in the electronic camera,
For the purpose of reducing noise, a small amplitude component of image data is smoothed in the time axis direction or the space axis direction. Further, in the electronic camera and the image processing program, fixed pattern noise of the image pickup device is removed from the image data for the purpose of reducing noise. In the image data subjected to such noise reduction, the amount of noise of the image is reduced, so that the compression code amount is reduced. Therefore, the following compression coding is preferable under the imaging condition for executing any noise reduction or the imaging condition for strongly performing the noise reduction. ● In the trial step, the compression parameter for trial is set to a low degree of compression. ● In the parameter estimation step, using the statistical relationship obtained for smoothed image data of minute amplitude components (more specifically, image data subjected to noise reduction, image data subjected to strong noise reduction), Estimate compression parameters. ● In the compression step, the compression parameters are calculated by using the correction processing obtained for the image data in which the small amplitude component is smoothed (more specifically, the image data subjected to noise reduction and the image data subjected to strong noise reduction). To correct. ● Standard quantization table suitable for smoothed image data of minute amplitude components (more specifically, image data subjected to noise reduction, image data subjected to strong noise reduction) based on subjective evaluation of image quality Alternatively, the distribution of quantized coefficients is determined. The standardized quantization table or the distribution of the quantization coefficients is used in correspondence with the imaging condition for executing the noise reduction or the imaging condition for strongly applying the noise reduction.

[Z] Wide Dynamic Range Mode The electronic camera executes a mode (wide dynamic range mode) for combining image data captured a plurality of times with different exposures for the purpose of increasing the gradation of a captured image. In the image data processed in this way, since the gradation information of the image increases, the compression code amount increases. Therefore, under the selected imaging condition of the wide dynamic range mode, the following compression coding is preferable. ● In the trial step, set the compression parameter for trial to have a high degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data processed in the wide dynamic range mode. In the compression step, the compression parameter is corrected using the correction processing obtained for the image data processed in the wide dynamic range mode. ● Based on subjective evaluation of image quality, determine a standard quantization table or quantization coefficient distribution suitable for image data processed in wide dynamic range mode. This standardized quantization table or the distribution of quantization coefficients is used according to the imaging conditions of the wide dynamic range mode.

[Α] Number of Output Pixels In an electronic camera, the number of pixels of image data can be converted and the number of output pixels can be changed appropriately. When the number of output pixels is changed in this way, the compression code amount changes significantly due to factors such as a change in the data amount and a shift of the spatial frequency component in the frequency axis direction. Therefore, by grouping the image data according to the imaging condition of the number of output pixels, the following more appropriate compression encoding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[Β] Photometric Mode The electronic camera has selectable photometric modes for determining the exposure, such as a multi-pattern photometric mode, a center-weighted photometric mode, and a spot photometric mode. Among these, the case where the spot metering mode is selected is a subject that is difficult to be metered normally due to the extreme brightness and darkness, and the photographer intentionally adjusts the exposure to a non-average brightness area in the image capturing screen. In many cases For this reason, there is a high possibility that white crushing or black crushing will occur outside the photometric area of spot metering. When white crushing or black crushing occurs in this way, the amount of image information is lost, so the compression code amount becomes small. Therefore, the following compression coding is preferable under the image pickup condition in which the spot metering mode is selected. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data captured in the spot metering mode (more specifically, the image data having a lot of black and white shadows). In the compression step, the compression parameter is corrected by using the correction processing obtained for the image data captured in the spot metering mode (more specifically, the image data having a lot of black crushing or white crushing). ● Based on subjective evaluation of image quality, determine the standard quantization table or distribution of quantization coefficients suitable for image data captured in spot metering mode (more specifically, image data with a lot of blackout or whiteout). To do. This standardized quantization table or the distribution of the quantization coefficients is used according to the imaging conditions for which the spot metering mode is selected.

It should be noted that although not as good as the spot metering mode, the other characteristics of the metering mode also tend to be peculiar to the light and dark distribution of image data and the pattern. Therefore, a significant change occurs in the compression code amount of image data depending on the selected photometric mode. Therefore, by grouping the image data according to the selected type of photometric mode, the following appropriate compression encoding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[Γ] Presence / Absence of Fish-Eye Converter Lens An electronic camera can obtain image data of a circular screen by mounting a fish-eye converter lens on a photographing lens. Since such image data does not have image information around the circular screen, the compression code amount is small.
Therefore, the following compression encoding is preferable under the image pickup condition in which the fisheye converter lens is mounted. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained with respect to the image data on the circular screen (more specifically, the image data imaged by using the fisheye comparator lens). In the compression step, the compression parameter is corrected by using the correction process obtained for the image data of the circular screen (more specifically, the image data imaged by using the fisheye comparator lens). ● Based on subjective evaluation of image quality, determine standard quantization table or distribution of quantization coefficients suitable for circular screen image data (more specifically, image data captured using a fisheye comparator lens). To do. This standardized quantization table or distribution of quantization coefficients is used, depending on the imaging conditions in which the fisheye converter lens is used. The presence / absence of the fisheye converter lens may be detected by, for example, user input, automatic detection by data communication between the electronic camera and the comparator lens, or automatic identification of whether the image data is a circular screen.

[Δ] Presence / Absence of Teleconverter Lens The electronic camera can obtain telephoto image data by mounting the teleconverter lens on the photographing lens. In such image data, the background portion on the screen is easily blurred. As a result, the high spatial frequency component of the image data is lost, and the compression code amount becomes small. Therefore, under the imaging conditions where the teleconverter lens is attached,
The following compression coding is preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, an appropriate compression parameter is estimated using the statistical relationship obtained for the image data with large blur (more specifically, the image data captured by using the teleconverter lens). In the compression step, the compression parameter is corrected using the correction process obtained for the image data with large blur (more specifically, the image data captured by using the teleconverter lens). ● Based on subjective evaluation of image quality, determine a standard quantization table or distribution of quantization coefficients suitable for image data with large blur (more specifically, image data captured using a teleconverter lens). This standardized quantization table or distribution of quantization coefficients is used according to the imaging conditions in which the teleconverter lens is mounted. The presence / absence of the teleconverter lens may be detected by, for example, user detection, automatic detection by data communication between the electronic camera and the comparator lens, or the like.

[Ε] Presence / Absence of Wide Converter Lens In the electronic camera, it is possible to obtain wide-angle image data by mounting the wide converter lens on the photographing lens. In such image data, the background portion on the screen is less likely to be blurred. As a result, the high spatial frequency component of the image data becomes large and the compression code amount becomes large. Therefore, the following compression coding is preferable under the image pickup condition in which the wide converter lens is mounted. ● In the trial step, set the compression parameter for trial to have a high degree of compression. In the parameter estimation step, an appropriate compression parameter is estimated using the statistical relationship obtained for the image data with a small blur (more specifically, the image data captured using the wide converter lens). In the compression step, the compression parameter is corrected by using the correction process obtained for the image data with small blur (more specifically, the image data captured by using the wide converter lens). ● Based on subjective evaluation of image quality, a standard quantization table or distribution of quantization coefficients suitable for image data with small blur (more specifically, image data captured using a wide converter lens) is determined. This standardized quantization table or the distribution of the quantization coefficients is used according to the imaging condition in which the wide converter lens is mounted. The presence / absence of the wide converter lens may be detected by, for example, automatic detection by user input, data communication between the electronic camera and the comparator lens, or the like.

[Ζ] Presence / Absence of Optical Filter and Kind of Optical Filter The electronic camera can obtain various image data by mounting the optical filter on the taking lens.
For example, by attaching an optical filter of a soft focus system or an image flow system, it is possible to obtain fantastic image data.
Such image data has a small high spatial frequency component,
The compression code amount becomes small. Further, for example, by attaching an optical filter of a specific color, it is possible to obtain image data in which the hue is biased. In such image data, the width of hue change on the entire screen is narrow and the compression code amount is small. Further, for example, by mounting an ND (Neutral Density) filter, it is possible to extend the exposure time and obtain image data in which subject blurring is emphasized. Such image data has a small high spatial frequency component and a small compression code amount.

Therefore, under the image pickup conditions in which the above optical filter is mounted, the following compression coding is preferable. ● In the trial step, the compression parameter for trial is set to a low degree of compression. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained for the image data captured with the optical filter attached. In the compression step, the compression parameter is corrected by using the correction processing obtained for the image data captured by mounting the optical filter. ● Based on subjective evaluation of image quality, determine a standard quantization table or distribution of quantization coefficients suitable for image data captured with an optical filter attached. This standardized quantization table or the distribution of quantization coefficients is used according to the image pickup condition in which the above optical filter is mounted.

The compression code amount of image data also varies significantly depending on the type of the comparator lens or the optical filter (including the strength of the filter effect). Therefore, by grouping the image data according to the type of the mounted converter lens or optical filter, the following more appropriate compression encoding can be performed. In the trial step, the standard compression parameter obtained in advance for the image data in the same group is used as the trial compression parameter. In the parameter estimation step, the compression parameter is estimated using the statistical relationship obtained in advance for the image data in the same group. ● In the compression step, the compression parameters are corrected using the correction processing obtained for the image data in the same group. ● Determine the appropriate standard quantization table or distribution of quantization coefficients for each group based on subjective evaluation of image quality. At the time of compression encoding, the standard quantization table or the distribution of quantization coefficients is used properly according to the grouping of image data.

[0112]

[0113] In the compression coding method according to claim 1, separately for each imaging condition to prepare the "statistical relationship between the compression parameters and the compression code amount". Therefore, an accurate statistical relationship that reflects the characteristics of the imaging condition is obtained, and it is possible to accurately estimate the compression parameter.

In the compression coding method according to the second aspect , the compression parameter estimated from the result of the trial compression is corrected according to the imaging condition. Therefore, it is possible to strictly correct the estimation error of the compression parameter due to the imaging condition.

[0115]

[0116]

The compression encoding method described in claim 3 uses at least one of the following imaging conditions. ○ Imaging sensitivity setting ... Under these imaging conditions, the amount of noise in the image data changes mainly. Therefore, it is possible to perform the compression coding adapted to the change of the noise amount. ○ Signal gain: Under this imaging condition, the amount of noise of image data mainly changes. Therefore, it is possible to perform the compression coding adapted to the change of the noise amount. ○ Gamma correction curve: Under these imaging conditions, the noise amount and brightness gradation of the image data mainly change. Therefore, compression coding adapted to these changes is possible. ○ Presence or absence of electronic zoom ・ ・ Under these imaging conditions, the actual resolution of the image data changes. Therefore, it is possible to carry out compression encoding adapted to a substantial change in resolution. ○ Magnification of electronic zoom ··· Under these imaging conditions, the substantial resolution of image data changes. Therefore, it is possible to carry out compression encoding adapted to a substantial change in resolution. ○ Shutter speed: Under these imaging conditions, the amount of image blur mainly changes. In addition, the amount of noise in the image data also increases due to the increased storage time in the image capturing unit. Therefore, it is possible to perform compression coding that adapts to changes in the amount of blur and the amount of noise. ○ White balance adjustment value ･ ･ ･ The shooting location and shooting time of the image data can be estimated mainly from these shooting conditions.
Therefore, it is possible to perform the compression coding adapted to the change of the imaging location or the imaging time. ○ Special shooting effect ・ ・ From these shooting conditions, you can infer the characteristics of the image data that occur for each special shooting effect. Therefore, it is possible to perform compression coding adapted to each special photographing effect. ○ Gradation ··· Under these imaging conditions, the contrast and details of image data mainly change. Therefore, compression coding adapted to these changes is possible. Edge enhancement: Under these imaging conditions, the spatial frequency component of the image data changes mainly. Therefore, it is possible to perform the compression coding adapted to the change of the spatial frequency component. ○ Monochrome mode: Under these imaging conditions, the presence or absence of color information mainly changes. Therefore, it is possible to perform the compression encoding adapted to the presence or absence of the color information. ○ Exposure correction value: From this imaging condition, the tendency and characteristics of the subject to be exposure-corrected can be inferred. Therefore, it is possible to perform the compression coding adapted to the tendency and characteristics of the subject whose exposure is to be corrected. Noise reduction mode: Under these imaging conditions, the amount of noise in the image data changes mainly. Therefore,
It is possible to perform compression coding that adapts to changes in the amount of noise. Wide dynamic range mode: Under this imaging condition, mainly the amount of gradation information of image data changes. Therefore, it is possible to perform compression coding that adapts to changes in the amount of gradation information. ○ Number of output pixels: Under these imaging conditions, mainly the data amount of image data and spatial frequency components change. Therefore, compression coding adapted to these changes is possible.

The compression coding method according to claim 4 uses at least one of the following imaging conditions. ○ Whether or not strobe is used ・ Under these imaging conditions, the degree of occurrence of blackout in the background and image blur mainly change. Therefore, compression coding adapted to these changes is possible. ○ Whether or not slow sync is used: Under this imaging condition, the blackout occurs less frequently than just using a flash. Therefore, the compression coding adapted to such a change becomes possible. ○ Whether or not the daytime sync is used ・ Under these imaging conditions, the frequency of black crushing is extremely low compared to using a simple strobe. Therefore, the compression coding adapted to such a change becomes possible. O Photometric value: From this imaging condition, it is possible to infer the characteristics of image data that differ for each photometric value. Therefore, it is possible to perform the compression coding adapted to the change of the image data due to the photometric value. ○ Multi-pattern photometric value ・ ・ From this imaging condition, the light distribution of the subject can be estimated. Therefore, it is possible to perform the compression coding adapted to the change of the image data due to the light distribution state. ○ Distribution state of the subject ・ The light distribution state of the subject can be known from these imaging conditions. Therefore, it is possible to perform the compression coding adapted to the change of the image data due to the light distribution state. ○ Whether or not it is in vertical position shooting ... Under these imaging conditions, the screen configuration mainly changes. Therefore, it is possible to perform compression coding that adapts to changes in the screen configuration. ○ Camera blur amount: Under these imaging conditions, the amount of image blur mainly changes. Therefore, it is possible to perform the compression coding adapted to the change of the blur amount. ○ Temperature ... Under these imaging conditions, the amount of noise in the image data changes. Therefore, it is possible to perform the compression coding adapted to the change of the noise amount. ○ Metering mode: From this imaging condition, it is possible to infer the subject situation such as the peculiarity of the light distribution state of the subject and the possibility that the gradation of light and dark is lost. Therefore, it is possible to perform the compression coding adapted to these subject situations.

The compression coding method according to the fifth aspect uses at least one of the following imaging conditions. ○ Presence / absence of macro photography ... From these imaging conditions, it is possible to infer the change in image data due to the presence / absence of macro photography. Therefore, compression coding adapted to this change is possible. Image magnification: From this imaging condition, it is possible to infer changes in image data due to image magnification. Therefore, compression coding adapted to this change is possible. ○ Depth of field ··· Under these imaging conditions, the amount of blurring on the screen mainly changes. Therefore, it is possible to perform the compression coding adapted to the change of the blur amount. ○ Aperture value ... Under these imaging conditions, mainly the amount of blurring in the screen changes. Therefore, it is possible to perform the compression coding adapted to the change of the blur amount. ○ Focal length: Under these imaging conditions, the depth of field, image magnification, composition (perspective), etc. mainly change. Therefore, compression coding adapted to these changes is possible. ○ Shooting angle of view: Under these imaging conditions, the depth of field, image magnification, composition (perspective), etc. mainly change. Therefore, compression coding adapted to these changes is possible. ○ Subject distance: Under these imaging conditions, the depth of field, image magnification, composition (perspective), etc. mainly change. Therefore, compression coding adapted to these changes is possible. Focusing condition: The amount of blurring within the screen can be known mainly from this imaging condition. Therefore, it is possible to perform the compression coding adapted to the change of the blur amount. ○ Focusing condition of multiple points: From this imaging condition, the blur area and blur position occupying the screen can be estimated. Therefore, compression coding adapted to these changes is possible. ○ Shooting lens type ･ ･ ･ From these shooting conditions, changes in image data depending on the shooting lens type can be seen. Therefore, compression coding adapted to this change is possible. ○ Presence / absence and type of comparator lens ... From these imaging conditions, changes in image data due to presence / absence and type of converter lens can be known. Therefore, compression coding adapted to this change is possible. ○ Presence or absence of optical filter and type
The change of image data depending on the presence or absence of the optical filter and the type can be understood. Therefore, compression coding adapted to this change is possible.

A compression encoding program is recorded on the recording medium according to the sixth aspect . By executing this compression encoding program on a computer, the compression encoding method according to any one of claims 1 to 5 can be realized on the computer.

An electronic camera according to a seventh aspect is the electronic camera according to the first aspect.
The compression encoding method according to any one of claims 5 to 5 can be applied to the captured image data.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of an electronic camera.

FIG. 2 is a flowchart showing a procedure of preparatory steps for compression encoding in the first embodiment.

FIG. 3 is a data table of an initial scale factor ISF.

FIG. 4 (ISO200) and (without strobe)
6 is a graph showing a relationship between a scale factor and a compression code amount for a test image taken under the imaging condition of FIG.

FIG. 5 is a graph showing a relationship between a scale factor and a compression code amount for a test image captured under ISO1600 imaging conditions.

FIG. 6 is a graph in which undetermined coefficients a and b are plotted.

FIG. 7 is a flowchart illustrating a compression encoding method according to the first embodiment.

FIG. 8 is a graph showing a correlation between a target scale factor estimated from one trial compression and an accurate scale factor (actual measurement value) for obtaining a compression rate of 1/4.

FIG. 9 is a flowchart showing a procedure of preparatory steps for compression encoding in the second embodiment.

FIG. 10 is a flowchart illustrating a compression encoding method according to the second embodiment.

FIG. 11 is a graph showing a relationship between a scale factor and a compression code amount for a test image captured using a strobe.

FIG. 12 is a data table of an initial scale factor ISF.

FIG. 13 is a graph showing a correlation between a target scale factor estimated from one trial compression and an accurate scale factor (actual measurement value) for obtaining a compression rate of ¼.

FIG. 14 is a flowchart showing a procedure of preparatory steps for compression encoding in the third embodiment.

FIG. 15 is a diagram showing an example of a standard quantization table corresponding to imaging sensitivity setting.

FIG. 16 is a flowchart showing a compression encoding method in the third embodiment.

[Explanation of symbols]

10 electronic camera 11 Shooting lens 12 Strobe emission part 13 Image sensor 15 Signal processing unit 16 A / D converter 17 Image processing section 18 compression processing unit 21 Control unit 22 Multi-photometer 23 Focus detection unit 24 operation buttons

─────────────────────────────────────────────────── ─── Continuation of front page (51) Int.Cl. ⁷ Identification code FI H04N 5/92 H04N 5/92 H (56) Reference JP-A-3-65886 (JP, A) JP-A-3-230691 ( JP, A) JP 8-181908 (JP, A) JP 7-67032 (JP, A) JP 7-177463 (JP, A) JP 9-186919 (JP, A) JP Japanese Patent Laid-Open No. 4-20190 (JP, A) Japanese Patent Laid-Open No. 10-150633 (JP, A) Japanese Patent Laid-Open No. 11-88825 (JP, A) Japanese Patent Laid-Open No. 7-75056 (JP, A) Japanese Patent Laid-Open No. 4-333987 (JP , A) JP-A-3-303480 (JP, A) JP-A-6-86214 (JP, A) (58) Fields investigated (Int.Cl. ⁷ , DB name) H04N ^7/ 24-7-68 H04N 1/41-1/419 H04N 5/91-5/95 H03M 7/30-7/38 H04N 5/225-5/243

Claims (7)

(57) [Claims] 1. An image is compressed using a trial compression parameter.
A trial step of compression-encoding image data, and a compression result of the image data in the trial step
Is obtained by compression-encoding multiple image data in advance on a trial basis.
"Statistical relationship between compression parameter and compression code amount"
And compress the image data to the target code amount.
Parameter estimation step to estimate the compression parameters for
And the compression parameter estimated in the parameter estimation step
Is used to compress and encode the image data.
In the compression encoding method including the step of estimating the statistical relationship obtained for each variation of imaging conditions.
Prepared in association with the imaging conditions, and
Select and use the above statistical relationships according to the imaging conditions of the data
A compression encoding method characterized by: However, the above shooting
The image conditions are set conditions when the image data is captured or
Is the condition of the shooting environment and there is a significant difference in the image data.
What happens and the difference affects the compression code amount
Is. Note that the imaging conditions are the target compression rate and the target compression.
Image compression conditions such as code amount and image data after imaging
Is different from the actual state (high frequency component of the image signal, etc.). Furthermore
In the above, the compression parameter is a compression encoding process.
Is an adjustable element that affects the compression code amount in
It 2. A compression parameter for trial is used to
The trial step of compressing and encoding the image data, and the compression result of the image data in the trial step
Based on the above, the image data is compressed to a target code amount.
Parameter estimation step to estimate the compression parameters for
And the compression parameter estimated in the parameter estimation step
Is used to compress and encode the image data.
In the compression encoding method including the step, the compression step includes "the compression parameter obtained in the parameter estimation step".
“Compression parameter for compression to the target compression code amount
The corrective value obtained from the relationship between
Prepare the image data according to the variation
Using the correction processing corresponding to the imaging conditions of
Correct the compression parameter estimated in the meter estimation step.
A compression encoding method characterized by the following. However, the imaging
The conditions are set conditions when the image data is captured or
It is a condition of the shooting environment and produces a significant difference in the image data.
However, the difference affects the compression code amount.
is there. The imaging conditions are the target compression rate and the target compression code.
Image compression conditions, such as
It is different from the actual state (high-frequency components of the image signal, etc.). Furthermore
In the above, the compression parameter is a compression encoding process.
Is an adjustable element that affects the compression code amount in
It 3. The compression encoding method according to claim 1 , wherein the image pickup condition is a condition of an image pickup unit that picks up the image data, that is, image pickup sensitivity setting and signal gain. , Gamma correction curve, presence or absence of electronic zoom, electronic zoom magnification, shutter speed, white balance adjustment value, special shooting effect, gradation, edge enhancement, monochrome mode, exposure correction value, noise reduction mode, wide dynamic range mode, output A compression encoding method characterized by being at least one of the number of pixels. 4. The compression encoding method according to claim 1 , wherein the imaging condition is a condition of an imaging environment in which the image data is imaged, whether or not a strobe is used, and a slow speed. It is characterized by at least one of whether or not synchro is used, whether or not daytime synchro is used, photometric value, multi-pattern photometric value, subject light distribution state, vertical shooting, camera shake amount, temperature, and photometric mode. Compression encoding method. 5. The compression encoding method according to claim 1 , wherein the imaging condition is a condition of a photographic lens that has captured the image data, whether or not a macro image is captured, and an image. Magnification, depth of field, aperture value, focal length, shooting angle of view, subject distance, focus situation, multi-point focus situation,
A compression encoding method, which is at least one of the type of photographing lens, the presence or absence of a converter lens, the type of converter lens, the presence or absence of an optical filter, and the type of an optical filter. 6. A machine-readable recording medium recording a compression encoding program for causing a computer to execute the compression encoding method according to any one of claims 1 to 5 . 7. The compression code according to claim 1, wherein the image pickup unit picks up an image of a subject to generate image data, and the image data generated by the image pickup unit. An electronic camera, comprising: