JP2011254215A - Image coding method, image decoding method, image coding device, image decoding device, and program for the method and device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To realize coding which reduces the amount of codes while retaining a subjective image quality.SOLUTION: An image signal of an original signal is subjected to bit depth conversion (S10), and a signal expressed by a bit depth less than that of the original signal (structure component) is subjected to coding processing based on waveform coding (S11). A signal of a texture component obtained by removing a signal of the structure component from the original signal is subjected to coding processing based on texture analysis (S16), and coded data by the waveform coding processing and coded data by the coding processing based on the texture analysis are integrated to a coded stream.

Description

  The present invention relates to a high-efficiency image signal encoding method and decoding method.

  H. In the current encoding method represented by H.264 / AVC, a prediction signal is generated by intra-frame prediction, inter-frame prediction, and inter-layer prediction, and a residual signal between the prediction signal and the original signal is to be encoded. Such a conventional technique aims to accurately reproduce the original signal waveform. However, in such a conventional method, since the code amount is controlled by quantization under the waveform reproduction framework, image quality degradation such as block distortion and ringing becomes obvious at a low rate.

  Further, since the prediction process refers to the decoded signal, at a low rate, the deteriorated decoded signal is referred to, and it is impossible to predict a high frequency component above a certain level. For this reason, the prediction performance deteriorates for an image containing many high-frequency components. For example, such low-rate image quality degradation is noticeable in scenes such as water surfaces where a rigid body movement model is not established in the case of inter-frame prediction, and in the case of intra-frame prediction and inter-layer prediction, it is noticeable in the texture area of a fine pattern. Become.

  On the other hand, a technique called texture synthesis is being studied. This is a technique for synthesizing an image based on a predetermined generation rule using given data (referred to as a seed). The texture synthesis is different from the conventional general coding method in that it does not pursue accurate waveform reproduction of the original signal but uses the texture feature quantity in the original signal as a reproduction target.

  As a texture synthesis method, for example, there is a method described in Non-Patent Document 1. In this method, a histogram of coefficients obtained as an output of the directional filter bank is used as a texture feature value as an input of the texture synthesis process.

D. Heeger, and J. Bergen, “Pyramid-Based Texture Analysis / Synthesis”, Proc ACM SIGGRAPH 95, 1995.

  In general, the amount of seed data used in texture synthesis can be suppressed to be extremely small compared to the amount of data of the original signal. Therefore, application of texture synthesis to image coding is expected.

  On the other hand, the similarity between the signal generated by texture synthesis and the original signal is not guaranteed from the viewpoint of waveform reproduction. This is because there is no guarantee that the phase information in the image is retained. For this reason, if it is required to obtain a signal with an image quality that is subjectively similar to the original signal on the premise of comparison with the original image, even if the texture synthesis method is simply applied to image coding, the request Can not respond to.

  The present invention has been made in view of such circumstances, and as a low-rate encoding method, a method that replaces the conventional waveform reproduction-based encoding method, based on texture synthesis technology, is subjectively close to the original image quality. It aims at establishing the process which produces | generates the signal of.

  Waveform reproduction using only texture synthesis has its limitations due to its processing structure. This is because the phase information in the image is not retained in texture synthesis. In the present invention, a conventional waveform reproduction-based encoding method and texture synthesis are combined, and an approach is taken in which the weak points of both are supplemented by the advantages of each other. For this purpose, the image signal is divided into components that represent the general shape of the waveform (structural components) and other components (texture components), and waveform reproduction based coding is applied to the structural components. Encoding by texture analysis and texture synthesis is applied.

  That is, the present invention divides an image signal to be encoded into a structural component and a texture component in order to reduce the amount of code while maintaining subjective image quality, and the structural component is encoded based on waveform encoding. The texture component is encoded based on texture synthesis. When dividing an image signal into a structural component and a texture component, for example, the upper bits of the image signal are used as structure information and the lower bits are used as texture information. A large code amount reduction effect can be expected for information in which deterioration of lower bits is difficult to detect visually.

  Specifically, the image coding apparatus of the present invention first performs coding processing based on waveform coding on a signal expressed with a small bit depth by bit depth conversion of the original signal to generate coded data. The encoded data is decoded, and a signal having the same bit depth as the original signal is restored by inverse bit depth conversion. The difference signal between the restored signal and the original signal is calculated, the bit depth of the difference signal is limited as necessary, and texture analysis is performed on the difference signal of which the bit depth is limited. Is expressed by a histogram of pixel values and a group of histograms of transform filter coefficients to generate encoded data by texture analysis. The encoded data by the above waveform encoding process and the encoded data by texture analysis are integrated and output as an encoded stream.

  Alternatively, the image coding apparatus of the present invention separates the upper bits and the lower bits of the input image, and the upper bits are expressed by a bit shift as a signal having a bit depth smaller than that of the original signal. Encoding processing based on is performed to generate encoded data. Also, by performing texture analysis on the lower-order bit signal, the signal is expressed by a histogram of pixel values and a histogram group of transform filter coefficients to generate encoded data by texture analysis. The encoded data by the above waveform encoding process and the encoded data by texture analysis are integrated and output as an encoded stream.

  In the image coding, it is also preferable to control the accuracy of expression of the histogram obtained by texture analysis according to the bit depth limit amount for the difference signal.

  In addition, the image decoding apparatus of the present invention receives encoded data obtained by waveform encoding processing as an input, generates a decoded signal by decoding processing, and performs the same bit depth as the original signal by inverse bit depth conversion on the decoded signal. Is restored as a signal with. Also, the encoded data obtained by texture analysis is input, the residual signal that cannot be expressed by the waveform encoding process is decoded by the texture synthesis process, and the decoded signal is obtained by integrating the two signals.

  According to the present invention, it is possible to realize encoding that reduces the code amount while maintaining subjective image quality.

It is a figure which shows the structural example of an image coding apparatus. It is a figure which shows the structural example of the analysis filter of Steerable pyramid. It is a figure which shows the other structural example of an image coding apparatus. It is a figure which shows the other structural example of an image coding apparatus. It is a figure which shows the other structural example of an image coding apparatus. It is a figure which shows the structural example of an image decoding apparatus. It is a figure which shows the structural example of the synthesis filter of Steerable pyramid. It is a figure which shows the other structural example of an image decoding apparatus. It is a flowchart of an encoding process. It is a flowchart of a texture analysis process. It is a flowchart of a decoding process. It is a flowchart of a texture synthetic | combination process.

  Hereinafter, an outline of processing performed in the present embodiment will be described.

FIG. 1 shows a configuration example of an image coding apparatus according to an embodiment of the present invention. The flow of the encoding process will be described with reference to FIG. First, the image encoding device 1 inputs an image signal (N-bit signal) to be encoded. The bit depth conversion unit 10 receives this image signal (N-bit signal) and converts it into an N-Δ bit signal by bit depth conversion processing. Here, Δ is an integer satisfying 1 ≦ Δ ≦ N. As the bit depth conversion processing, there is a method of shifting each pixel value to the right by Δ bits. Note that this calculation result matches the value obtained by multiplying the original pixel value by 1 / (2 to the power of Δ) and truncating the decimal part. There is also a method of approximating 2 ^N types of pixel values in an N-bit signal with “2 (N−Δ)” types of pixel values based on the norm of square error minimization and using the concept of quantization. is there.

  The N-Δ bit signal obtained as the output of the bit depth conversion process is a structural component in this method. Therefore, the waveform encoding processing unit 11 performs waveform encoding processing on the N-Δ bit signal which is a structural component, and outputs an encoded stream. As the waveform encoding process, H.264 has been described. H.264 / AVC-compliant encoding scheme is used. In the present invention, the waveform encoding process is H.264. For example, methods such as JPEG, JPEG2000, and MPEG-2 can be used.

  Next, the decoding processing unit 12 applies H.264 to the encoded stream obtained as the output of the waveform encoding process. A decoding process defined by H.264 / AVC is performed to obtain a decoded signal. As an encoding method, H.264 is used. When a method other than H.264 / AVC (JPEG, JPEG2000, MPEG-2, etc.) is used, a corresponding decoding process is performed.

  The inverse bit depth conversion unit 13 performs an inverse bit depth conversion process on the decoded signal (N-Δ bit signal) obtained as an output of the decoding process, and obtains a decoded signal of a structural component as an N bit signal.

The difference signal generation unit 14 calculates a difference signal between the original signal and the decoded signal (N-bit signal) of the structural component. This difference signal becomes a texture component in this method. The texture analysis processing unit 15 performs texture analysis processing on the texture components, and extracts the following two types of histograms as information necessary for texture synthesis at the time of decoding. One is a difference signal histogram. The other is a histogram for transform coefficients obtained by Steerable pyramid transform (see Reference 1) for differential signals.
[Reference 1]: EP Simoncelli and WT Freeman, “The Steerable Pyramid: A Flexible Architecture for Multi-Scale Derivative Computation”, IEEE Int'l Conf. On Image Processing. Washington DC, October 1995.
Steerable pyramid transform is a kind of directional filter bank, and its output is defined by the number of directions and the decomposition level. When Steerable pyramid transformation with the number of directions M and decomposition level J _max is performed with the original signal as 2 ^J × 2 ^J pixels, M types of direction components are used at the jth decomposition level (0 ≦ j ≦ J _max −1). Each directional component has 2 ^JJ × 2 ^Jj conversion coefficients. At the highest decomposition level j = J _max , there is no division with respect to the direction component, and 2 ^J−Jmax × 2 ^J−Jmax conversion coefficients. In addition to the above, 2 ^J × 2 ^J conversion coefficients are provided as high frequency components.

  Further, since the transform coefficient is expressed by a real value, each direction component for each decomposition level is quantized with a predetermined quantization width K, and a histogram for the quantized transform coefficient is used as texture analysis information.

As an example, FIG. 2 shows a configuration example of an analysis filter for steerable pyramid conversion when M = 2 and J _max = 1. The input of the high-pass filter 21 and the low-pass filter 22 is an image signal (difference signal). This analysis filter performs specified level decomposition and direction decomposition on the input image signal, and calculates filter coefficients as high-frequency components and low-frequency components. The high-pass filter 21 outputs a high-pass filter coefficient, the first-direction component pass filter 231 outputs a low-pass filter + first-direction component pass filter coefficient, and the second-direction component pass filter 232 The coefficient of the low-pass filter + first-direction component pass filter is output, and the low-pass filter + downsampled coefficient is output from the low-pass filter 233 and the downsampling unit 234.

  When increasing the decomposition level, the processing of the portion 23 surrounded by the broken line is recursively repeated for the downsampled signal output from the downsampling unit 234.

  FIG. 3 shows another configuration example of the image encoding device. In the image encoding device 1-1 illustrated in FIG. 3, a bit depth restriction processing unit 16 is provided between the difference signal generation unit 14 and the texture analysis processing unit 15 with respect to the image encoding device 1 illustrated in FIG. 1. It has been. Other components are the same as those of the image encoding device 1 shown in FIG. The bit depth restriction processing unit 16 extracts the lower δ bits of the difference signal and restricts the target of texture analysis processing to the δ bit signal. In this case, δ can be the same value as Δ, but can be set independently of Δ. This δ is called a bit depth limit parameter.

Here, the histogram representation methods are organized. When the histogram is expressed as h _B [i] (i = 0, 1,..., B−1), h _B [i] stores the number of samples whose target signal value is i. At this time, B is called the bin number of the histogram, and h _B [i] is called the frequency value of the i-th bin.

As described above, by limiting the bit depth of the texture synthesis target, the difference signal is limited to “2 to the δth power” type of pixel value. At this time, pixel values of the “2 to the δth power” type are stored in the corresponding histogram, and the bin number of the histogram can be suppressed from 2 ^{N + 1} to “2 to the δth power”. As a result, it is possible to reduce the amount of information expressing the histogram compared to before the bit restriction.

  Also for the histogram for the transform coefficient of the Steerable pyramid transform, the number of bins of the histogram is limited based on the bit depth limit parameter δ. Specifically, the bin number of the histogram is set to be smaller as the bit depth restriction parameter δ is smaller. In consideration of the fact that the bit depth required for the transform coefficient also changes according to the bit depth of the signal before the conversion, keeping the number of bins in the histogram of the transform coefficient is always constant when δ is small. This is because a redundant expression may be given to the coefficient.

A histogram for the transformation coefficient of the m-th (m = 0, 1,..., M−1) direction component at the j-th decomposition level (0 ≦ j ≦ J _max −1) in the Steerable pyramid transformation is expressed as L _x ^{(j, m)} [ i] (i = 0,..., 2 ^{2 (Jj)} −1) [where subscript X is a notation instead of Bδ (δ is a subscript), and so on). As described above, at the highest decomposition level j = J _max , there is no division related to the direction component, and it is limited to the histogram corresponding to m = 0. In addition to the above, since there are 2 ^J × 2 ^J conversion coefficients as high-frequency components, a histogram for storing these high-frequency components is represented as H _J [i] (i = 0,..., 2 ^2J −1). And

A histogram for transform coefficients of m-th (m = 0, 1,..., M−1) direction components at the j-th decomposition level (0 ≦ j ≦ J _max −1) is expressed as L _x ^{(j, m)} [i] (i = 0,..., 2 ^{2 (Jj)} −1) means that the conversion coefficient expressed by a real value is approximated by Bδ types of discrete values. In this discretization, the quantization width to be used is q _{j, m} , and the m-th (m = 0, 1,..., M−1) direction component conversion at the j-th decomposition level (0 ≦ j ≦ J _max −1). Assuming that the coefficient c _{j, m} [k] (k = 0,..., 2 ^{2 (Jj)} −1), the approximation error _Ex ^{(j, m)} accompanying this discretization is expressed as follows: .

In this equation, the value x sandwiched between the “L-shaped symbol” and the “left-right inverted L-shaped symbol” means rounding off the decimal point with respect to the real number x. In addition, the approximation error for high frequency components is expressed as follows for convenience.

Note that m = M used here is not used to identify a direction component but as an index representing a high-frequency component.

  The following equation is defined as the sum of approximation errors for the omnidirectional components at all resolution levels.

As the number of bins increases, the approximate error sum E _total (Bδ, δ) decreases monotonously. Therefore, when setting the number of bins, a threshold value T for the formula (1) is set, a plurality of values as candidates for the number of bins are prepared, and the maximum value satisfying the following formula among the values included in the candidates is set. Is the number of bins.

E _total (Bδ, δ) ≦ T
FIG. 4 shows another configuration example of the image encoding device. In the image encoding device 1-2 illustrated in FIG. 4, a bit depth restriction processing unit 17 is provided after the texture analysis processing unit 15 with respect to the image encoding device 1 illustrated in FIG. 1. Other components are the same as those of the image encoding device 1 shown in FIG. The bit depth restriction processing unit 17 restricts the output of the texture analysis processing unit 15 to a δ bit signal. By limiting to the δ-bit signal, it is possible to further compress the code amount of the encoded data by the texture analysis process.

  FIG. 5 shows still another configuration example of the image encoding device. In the image encoding device 1-3 illustrated in FIG. 5, the bit shift processing unit 101 extracts an upper N-Δ bit image signal (Δ> 0) from the input N-bit image signal. The waveform encoding processing unit 102 performs an encoding process based on the waveform encoding on the N-Δ bit image signal and outputs encoded data.

  On the other hand, the low-order bit extraction processing unit 103 extracts a low-order Δbit image signal from the input N-bit image signal, and the texture analysis processing unit 104 performs low-order Δbit image signal analysis by texture analysis on the low-order Δbit image signal. Coded data of texture analysis information expressing an image signal by a histogram of pixel values and a histogram group of transform filter coefficients is generated. The encoded data by these waveform encoding processes and the encoded data by texture analysis are integrated as an encoded stream.

  As described above, when bit shift is used as the bit depth conversion processing, the encoding processing shown in FIGS. 1, 3, and 4 can be realized in the format shown in FIG. The advantage of the configuration of FIG. 5 is that the texture analysis process and the waveform encoding process can be processed in parallel. Therefore, in the encoding process shown in FIGS. 1, 3 and 4, the texture synthesis process cannot be executed until the waveform encoding process / decoding process is completed. Therefore, the processing speed can be improved in an environment where parallel processing is possible.

  Decoding processing will be described with reference to FIG. FIG. 6 shows a configuration example of the image decoding apparatus. The image decoding device 3 includes a texture synthesis processing unit 30, a decoding processing unit 31, an inverse bit depth conversion unit 32, and a decoded signal generation unit 33.

  In the decoding process, two types of encoded information are input. First, the decoding processing unit 31 performs a corresponding decoding process on the encoded stream obtained as the output of the waveform encoding process to obtain a decoded signal. The inverse bit depth conversion unit 32 performs an inverse bit depth conversion process on the decoded signal (N-Δ bit signal) obtained as an output of the decoding process, and obtains a decoded signal of a structural component as an N bit signal.

  Next, the texture synthesis processing unit 30 receives the texture analysis information and decodes the texture information based on the following texture synthesis method. In the encoding process, when the bit depth limiting process as shown in FIGS. 3 and 4 is added, the texture information to be decoded is limited to δ bits.

  The method of Non-Patent Document 1 is used as the texture synthesis method. In this method, a histogram of coefficients obtained as an output of a directional filter bank called Steerable pyramid is used as an input for texture synthesis processing. The outline of this method is shown below.

  The input of the method includes an appropriate initial image (white noise image in Non-Patent Document 1), a pixel value histogram for a target texture image (hereinafter referred to as a target texture image), and each of the two images. Is a histogram of each subband. Steerable pyramid is used for subband decomposition. An image obtained as an output of texture synthesis is called a synthesized image. Note that, at the start of the following processing, the composite image is initialized with the initial image.

[Texture synthesis method]
(1) A white noise image as an initial image of the processing target image, pixel value histograms for the target texture image, and a histogram of conversion coefficients for each subband (referred to as a subband histogram) for both images are input. To do.

  (2) Based on the pixel value histogram of the composite image (initially the initial image) and the pixel value histogram of the target texture image, the pixel value histogram of the composite image is brought close to that of the target texture image.

  (3) A composite image is generated using the pixel value histogram of the composite image obtained in (2) above.

  (4) Using the composite image obtained in (3) above as an input, the directional filter bank is forward-converted with respect to the image and a conversion coefficient is calculated. A histogram of conversion coefficients is generated for each subband.

  (5) Using the subband histograms of both the composite image and the target texture image as input, the histogram of each subband of the composite image is brought close to the subband histogram of the same band of the target texture image.

  (6) Using the subband coefficient of the composite image corrected in (5) above as an input, the directional filter bank is inversely transformed and a composite image is output.

  (7) The processes (2) to (6) are repeated a certain number of times.

  As a technique for obtaining subband coefficients in texture synthesis, application of a filter bank other than the above, such as wavelet, is also being studied. The texture feature amount prediction mechanism according to the present invention is not limited to the above-described directional filter bank, and can be similarly applied to other filter banks.

As an example, FIG. 7 shows a configuration example of a synthesis filter for a steerable pyramid transformation when M = 2 and J _max = 1. This synthesis filter synthesizes an image signal from the input filter coefficients. The high-frequency synthesis filter 42 receives the filter coefficient output from the high-pass filter 21 shown in FIG. 2 and synthesizes an image signal. The first direction component synthesis filter 401 receives the coefficients of the low-pass filter and the first direction component pass filter output from the first direction component pass filter 231 shown in FIG. The second direction component synthesis filter 402 receives the coefficients of the low-pass filter and the second direction component pass filter output from the second direction component pass filter 232 shown in FIG. The up-sampling unit 403 receives the low-pass filter output from the down-sampling unit 234 shown in FIG. 2 and the down-sampled coefficient and performs up-sampling, and the low-frequency component synthesis filter 404 calculates the image signal from the up-sampled coefficient. Is synthesized. The low frequency synthesis filter 41 synthesizes the outputs of the first direction component synthesis filter 401, the second direction component synthesis filter 402, and the low frequency component synthesis filter 404. From the outputs of the high-frequency synthesis filter 42 and the low-frequency synthesis filter 41, the final differential image signal is synthesized.

  When increasing the decomposition level, the process of the portion 40 enclosed by the broken line is recursively repeated.

  An image decoding device 3-1 illustrated in FIG. 8 is a decoding device that performs a decoding process corresponding to the image encoding device 1-3 illustrated in FIG. In the image decoding device 3-1 shown in FIG. 8, an inverse bit shift processing unit 34 is used as the inverse bit depth conversion unit 32 in the image decoding device 3 of FIG. 6. The inverse bit shift processing unit 34 converts the N−Δ bit signal decoded by the decoding processing unit 31 into an N bit signal by performing reverse bit shift by Δ bits. The decoded signal generation unit 33 adds the N-bit signal and the Δ-bit signal output from the texture synthesis processing unit 30 to generate a decoded image of the N-bit signal.

  Hereinafter, a specific processing flow of the present embodiment will be described.

[Flowchart (encoding process)]
FIG. 9 is a processing flowchart of the image encoding device 1-1 shown in FIG. Other embodiments can be similarly processed.

(1) An image signal (N-bit signal) is input, and 2 ^N types of pixel values included in the signal are assigned to “2 to the (N−Δ) power” type of pixel value, and the signal is an N−Δ bit signal. Output (step S10).

  (2) An N-Δ bit signal is input, waveform encoding processing is performed according to a predetermined encoding method, and the encoded data is output. Examples of the waveform encoding process include H.264. There is an encoding method compliant with the H.264 / AVC standard (step S11).

  (3) The encoded data generated in (2) above is decoded to obtain a decoded signal as an N-Δ bit signal (step S12).

(4) An N-Δ bit decoded signal is input, and an inverse bit depth conversion process is performed in which “2 to the (N−Δ) power” type pixel value included in the signal is assigned to 2 ^N type pixel values. , N-bit decoded signals are output (step S13).

  (5) The difference between the decoded signal after inverse bit depth conversion generated in (4) above and the original signal is calculated and output as a difference signal (step S14).

  (6) The same differential signal is input, the bit depth is limited to δ bits, and output as a δ bit signal (step S15).

  (7) The texture analysis process is performed using the δ bit signal generated in (6) above as an input, and a histogram of pixel values and a histogram of filter coefficients for each band of the directional filter bank are output (step S16). The processing in step S16 is referred to as “texture analysis processing”.

[Flowchart (texture analysis processing)]
FIG. 10 is a detailed process flowchart of the texture analysis process (step S16).

  (1) The input signal is read and a histogram of pixel values of the input signal is output (step S161).

(2) The parameter direction number M that defines the Steerable pyramid transformation and the decomposition level J _max are read (step S162).

(3) Steerable pyramid conversion determined by the number of directions M and the decomposition level J _max is performed on the input signal, and a conversion coefficient is output (step S163).

(4) A histogram is obtained for 2 ^J × 2 ^J conversion coefficients belonging to the high frequency component, and output as H _J [i] (i = 0,..., 2 ^2J −1) (step S164).

(5) The following processing is repeated for j = 0, 1,..., J _max −1 (steps S165 to S170).

  (6) The following processing is repeated for m = 0, 1,..., M−1 (steps S166 to S168).

(7) A histogram for 2 ^{2 (Jj)} transform coefficients belonging to the mth direction component at the jth decomposition level is obtained, and L _x ^{(j, m)} [i] (i = 0,..., 2 ^{2 (Jj)} -1) (step S166).

(8) A histogram for “2 to the power of 2 (J−J _max )” belonging to the highest decomposition level j = J _max is obtained, and L _x ^Y [i] (i = 0,. 2 (J−J _max ) -1). Here, Y is (J _max , 0). At the highest decomposition level, there is no division related to the direction component, and it is limited to the histogram corresponding to m = 0 (step S171).

[Flowchart (decoding process)]
FIG. 11 is a processing flowchart of the image decoding device 3 shown in FIG. Other embodiments can be similarly processed.

  (1) The encoded data obtained as an output of the waveform encoding process is input, a decoding process corresponding to the waveform encoding process is performed, and a decoded signal is output (step S20).

  (2) Using the decoded signal (N-Δ bit signal) obtained as an output of the decoding process as an input, inverse bit depth conversion processing is performed to obtain a decoded signal of the structural component as an N-bit signal (step S21).

  (3) Using the texture analysis information as an input, texture synthesis processing is performed and a decoded texture signal is output (step S22). Details of this processing will be described later with reference to FIG.

  (4) The decoded signal of the structural component as an N-bit signal and the decoded texture signal are input, the two signals are added, and the signal after addition is output as a decoded signal (step S23).

[Flowchart (texture synthesis processing)]
FIG. 12 is a detailed process flowchart of the texture synthesis process (step S22).

  In the texture synthesis process, the histogram of the Steerable pyramid transform is input, and Steerable pyramid is used for subband decomposition. An image obtained as an output of texture synthesis is called a synthesized image. Note that, at the start of the following processing, the composite image is initialized with the initial image.

  (1) As input, a white noise image as an initial image of a processing target image, each pixel value histogram for a target texture image (hereinafter referred to as a target texture image), each subband for both images Read the histogram. This histogram is called a subband histogram (step S221).

  (2) The pixel value histogram of the composite image and the pixel value histogram of the target texture image are input, the pixel value histogram of the composite image is approximated to that of the target texture image, and the pixel value histogram of the composite image after processing is output (Step S222).

  (3) Using the pixel value histogram of the composite image obtained in (2) above, the pixel value of the processing target image is corrected and a composite image is generated (step S223).

  (4) Using the composite image obtained in (3) above as an input, the directional filter bank is forward-converted with respect to the image and a conversion coefficient is calculated (step S224).

  (5) Using the conversion coefficient of each subband as an input, a histogram of the conversion coefficient is output (step S225).

  (6) Using the subband histograms of both the composite image and the target texture image as input, the histogram of each subband of the composite image is approximated to the subband histogram of the same band of the target texture image, and the processed histogram Is output (step S226).

  (7) Using the subband histogram obtained in (6) above, the value of the transform coefficient is corrected to generate a corrected value (step S227).

  (8) Using the subband coefficient of the composite image corrected in (7) above as an input, the directional filter bank is inversely transformed and a composite image is output (step S228).

  (9) The above process is repeated a predetermined number of times (steps S222 to S229).

  The image encoding and image decoding processes described above can be realized by a computer and a software program, and the program can be recorded on a computer-readable recording medium or provided through a network. .

DESCRIPTION OF SYMBOLS 1 Image encoding apparatus 10 Bit depth conversion part 11 Waveform encoding process part 12 Decoding process part 13 Inverse bit depth conversion part 14 Differential signal generation part 15 Texture analysis process part 16, 17 Bit depth restriction | limiting process part 3 Image decoding apparatus 30 Texture Compositing processing unit 31 Decoding processing unit 32 Inverse bit depth conversion unit 33 Decoded signal generation unit 34 Inverse bit shift processing unit

Claims (10)

A process of inputting an N-bit image signal and converting the bit depth into an N-Δ bit signal (Δ>0);
A waveform coding process for performing coding processing based on waveform coding on the image signal of the N-Δ bit signal after bit depth conversion, and generating coded data;
Decoding the encoded data;
A process of inverse bit depth converting the decoded signal of the decoded N-Δ bit signal to generate an N-bit decoded signal;
Calculating a difference signal between the input N-bit image signal and the N-bit decoded signal after the inverse bit depth conversion;
A texture analysis process for generating encoded data of texture analysis information in which the difference signal is expressed by a histogram of pixel values and a histogram group of transform filter coefficients by texture analysis on the difference signal;
An image encoding method comprising: as an encoded stream, a process of integrating the encoded data by the waveform encoding process and the encoded data by the texture analysis process into an encoded stream. The image encoding method according to claim 1,
A bit depth limiting process for limiting the bit depth of the differential signal;
In the texture analysis process, encoded data is generated by texture analysis for a differential signal with a limited bit depth. The image encoding method according to claim 2, wherein
An image coding method, wherein the accuracy of representing a histogram obtained by the texture analysis is controlled according to a bit depth limit amount for the difference signal. A process of extracting an upper N-Δ bit image signal (Δ> 0) by bit shift from the input N-bit image signal;
A waveform coding process for performing coding processing based on waveform coding on the extracted N-Δ bit image signal and generating coded data;
Extracting a lower Δbit image signal from the N bit image signal;
A texture analysis process for generating encoded data of texture analysis information in which the lower Δbit image signal is represented by a histogram of pixel values and a histogram group of transform filter coefficients by texture analysis of the lower Δbit image signal;
An image encoding method comprising: as an encoded stream, a process of integrating the encoded data by the waveform encoding process and the encoded data by the texture analysis process into an encoded stream. A process of generating a decoded signal by the decoding process using the encoded data by the waveform encoding process as input;
Restoring the decoded signal as a signal having the same bit depth as the original signal before encoding by inverse bit depth conversion or bit shift;
The process of decoding the residual signal that could not be expressed by the waveform encoding process by the texture synthesis process using the encoded data from the texture analysis as input,
A process of generating a decoded signal by integrating two signals of a signal restored as a signal having the same bit depth as the original signal and a signal obtained by decoding the residual signal Method. A bit depth conversion unit that inputs an N-bit image signal and converts it into an N-Δ bit signal (Δ>0);
A waveform encoding processing unit that performs encoding processing based on waveform encoding on the image signal of the N-Δ bit signal after bit depth conversion, and generates encoded data;
A decoding processor for decoding the encoded data;
An inverse bit depth conversion unit that performs an inverse bit depth conversion on the decoded signal of the N-Δ bit signal that is an output of the decoding processing unit, and generates an N bit decoded signal;
A difference signal generation unit for calculating a difference signal between the input N-bit image signal and the N-bit decoded signal after the inverse bit depth conversion;
Texture analysis for generating encoded data of texture analysis information expressing the difference signal by a histogram of pixel values and a histogram group of transform filter coefficients by texture analysis on the difference signal or a difference signal with a limited bit depth of the difference signal A processing unit,
An image encoding device, wherein encoded data obtained by waveform encoding processing and encoded data obtained by texture analysis are integrated into an encoded stream as an encoded stream. A bit shift processing unit for extracting an upper N-Δ bit image signal (Δ> 0) from the input N-bit image signal;
A waveform encoding processing unit that performs encoding processing based on waveform encoding on the N-Δ bit image signal, and generates encoded data;
A lower bit extraction processing unit for extracting a lower Δbit image signal from the N bit image signal;
A texture analysis processing unit that generates encoded data of texture analysis information in which the lower Δbit image signal is expressed by a histogram of pixel values and a histogram group of transform filter coefficients by texture analysis of the lower Δbit image signal; Prepared,
An image encoding device, wherein encoded data obtained by waveform encoding processing and encoded data obtained by texture analysis are integrated into an encoded stream as an encoded stream. A decoding processing unit that receives encoded data obtained by waveform encoding processing as input and generates a decoded signal by decoding processing;
An inverse bit shift conversion unit that restores the decoded signal as a signal having the same bit depth as the original signal before encoding by inverse bit depth conversion or bit shift;
A texture synthesis processing unit that receives encoded data by texture analysis and decodes a residual signal that cannot be expressed by waveform coding processing by texture synthesis processing;
A decoded signal generating unit that generates a decoded signal by integrating two signals of a signal restored as a signal having the same bit depth as the original signal and a signal obtained by decoding the residual signal, An image decoding device.   An image encoding program for causing a computer to execute the image encoding method according to any one of claims 1 to 4.   An image decoding program for causing a computer to execute the image decoding method according to claim 5.

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