Optimizing the Quantization Parameters of the JPEG ...

REPRESENTATION, PROCESSING, ANALYSIS, AND UNDERSTANDING OF IMAGES

Optimizing the Quantization Parameters of the JPEG Compressor to a High Quality of Fine-Detail Rendition S. V. Sai Pacific National University, ul. Tikhookeanskaya 136, Khabarovsk, 680035 Russia e-mail: [email protected] Abstract—This paper describes a new algorithm for adaptive selection of DCT quantization parameters in the JPEG compressor. The quantization parameters are selected by classification of blocks based on the composition of fine details whose contrast exceeds the threshold visual sensitivity. Fine details are identified by an original search and recognition algorithm in the N-CIELAB normalized color space, which allows us to take visual contrast sensitivity into account. A distortion assessment metric and an optimization criterion for quantization of classified blocks to a high visual quality are proposed. A comparative analysis of test images in terms of compression parameters and quality degradation is presented. The new algorithm is experimentally shown to improve the compression of photorealistic images by 30% on average while preserving their high visual quality. Keywords: image analysis, identification of fine details, contrast sensitivity, and JPEG DOI: 10.1134/S1054661818010157

1. INTRODUCTION JPEG [1] is now the most popular method for digital compression of photorealistic images. Major quality losses with the JPEG algorithm occur at the stage of quantization of discrete cosine transform (DCT) coefficients:

Fi, j,k =

Q ⋅ Fi, j,k , M i, j

(1)

where Fi, j, k are the DCT coefficients obtained for the kth brightness or color block 8 × 8 pixels in size, Q is a quality parameter, and Mi, j are the elements of a quantization matrix whose integer values depend on the number of elements (i, j) in the block and can vary from 1 to 255. The JPEG standard recommends different quantization matrices for brightness and color blocks [2], where the values of the matrix elements increase with increasing i and j, which is due to certain properties of the spatial frequency response of vision. One of the JPEG features [2] is that users can employ their own quantization matrices and adaptively change their values depending on the structure of a block. This feature has been investigated by many specialists in the field of digital image processing, who try to improve the compression ratio while preserving the high quality of images. The well-known adaptive JPEG methods are based on tuning the quantizer’s parameters to a desired quality or compression level for identification of blocks with different levels of detail. The quantization matrix can be interpreted as a two-dimensional spatial-frequency low pass filter that

Received January 12, 2017

weakens the higher-frequency DCT coefficients of a block. Hence, a decreasing value of the parameter Q in (1) leads to a stronger suppression and zeroing of the high-frequency components of the block, which causes a greater distortion of the fine details and sharp edges in the image. An increase in Q reduces distortions, also causing the compression ratio to decrease. Thus, for adaptive quantization, at the first stage of processing, the image should be analyzed to identify blocks with different characteristics. Brightness and contrast are among these characteristics [3]. Some algorithms [4] employ methods for detecting the contours of details in the image. Segmentation methods for regions of interest (ROIs) that comprise objects and their background are also used [5]. Once blocks are identified and classified, discrete cosine transform and adaptive quantization of DCT coefficients are carried out:

Fi, j,k =

Qk ⋅ Fi, j,k , M i, j

(2)

where Qk is a quality parameter for the blocks of the class k. The values of Q vary in a range depending on the number of block classification levels. In the simplest case, only two levels (e.g., object and background) are used. The value of Q for objects is selected to be higher than that for the background. It should be noted that in some algorithms [5] the parameter Q is constant, while the quantization matrices change their values. In this paper, we propose an adaptive quantization of DCT coefficients based on an algorithm for search and recognition of fine details in the N-CIELAB normalized color space. A block classification algorithm is described and the experimental dependences of

ISSN 1054-6618, Pattern Recognition and Image Analysis, 2018, Vol. 28, No. 1, pp. 71–78. © Pleiades Publishing, Ltd., 2018.

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Table 1. Dependence of the weight coefficients on the size of a detail δ

>4

4

2

1

* Lth

1

1

3

6

ath*

2

10

20

40

bth*

3

15

30

55

2

K LAB

2

2

⎛ L* − L* ⎞ ⎛ a * − a * ⎞ ⎛ b * − b *⎞ f f f ⎟ +⎜ o ⎟ +⎜ o ⎟ , (4) = ⎜ o ⎜ L* (δ) ⎟ ⎜ a *(δ) ⎟ ⎜ b *(δ) ⎟ ⎝ th ⎠ ⎝ th ⎠ ⎝ th ⎠

where Lo* , ao* , and bo* are the color coordinates of a fine detail; L*f , a *f , and b f* are the color coordinates of the

image quality and compression ratio on selected quantization parameters are presented. 2. NORMALIZED COLORIMETRIC SYSTEM N-CIELAB The CIELAB colorimetric system is a popular international standard used to estimate color differences between original and distorted images [6]. These differences are evaluated as follows:

ΔE = ((L2* − L1*)2 + (a2* − a1*)2 + (b2* − b1*)2 ) ,

of fine details that takes weight coefficients of brightness and color into account:

(3)

where (L1* , a1* , b1* ) are the color coordinates of an object on the first (original) image and (L2* , a2* , b2* ) are the color coordinates of the object on the second (distorted) image. As test objects, color bands or fields with constant brightness and color for all pixels within the object are generally used [7]. The value ΔE ≈ 2.3 roughly corresponds to the minimum color difference perceived by the human eye. Color coordinates can be obtained by transforming the RGB colors into a color space (XYZ) and then by using the formulas L* = 116f(Y/Yn) – 16; a* = 500[f(X/Xn) – f(Y/Yn)]; b* = 200[f(Y/Yn) – f(Z/Zn)], where

⎧t1/3, if t > 0.008856 f (t ) = ⎨ ⎩7.787t + 16/116, if t ≤ 0.008856. The values Xn, Yn, and Zn are the coordinates of reference white. Equal-contrast color spaces CIELAB, CIELUV, CIEDE2000, etc., [7, 8] are traditionally used to estimate color distortions for large objects whose internal color distribution is uniform. An advantage of these color spaces is that the estimate depends only slightly on the color of the object. Obviously, the color difference estimate used in (3) proves to be incorrect when the size of an object decreases because it does not take into account the decrease in visual contrast sensitivity with respect to brightness and color. In [9, 10], a normalized system N-CIELAB was proposed with a metric for estimating color differences

background; Lth* (δ), ath*(δ), and bth*(δ) are the weight coefficients of brightness and color that depend on the number of MPCD thresholds; and δ is the size of fine details that is determined by the number of image pixels. In contrast to formula (3), formula (4) does not estimate color differences between two images; instead, it determines the normalized color contrast of a fine detail in one image. For a large detail, the weight coefficients are Lth* (δ) = ath*(δ) = bth*(δ) = 1 and, thereth fore, the threshold color contrast is K LAB = ΔE ≈ 2.3. As the size of the detail decreases, the threshold contrast grows due to the decrease in visual contrast sensitivity. The weight coefficients in formula (4) are selected in such a way that the normalized threshold th contrast is K LAB = 1. In this case, the following condition holds: if the contrast of a fine detail exceeds the th threshold K LAB > K LAB = 1, then the detail is visually perceivable against the background. For an experimental estimation of the weight coefficients, we developed test tables with fine details on an achromatic background. The contrast of fine details was specified individually by three coordinates: L*, a*, and b*. To render the image into the *.bmp format, the color coordinates L*, a*, and b* were converted pixel by pixel into RGB. In our experiments, we employed the following technique. Each experiment used test tables with fixed sizes of fine details: 1 pixel, 2 × 2 pixels, and 4 × 4 pixels. The spatial position of the details and their number were selected randomly. The lightness of the background was set as an average value of L*f = 50. Using the program interface, for a selected table, the observer varied the contrast by the coordinate L* (a* or b*) from zero to a certain value for which fine details became visually perceivable against the background. Finally, as a result of estimation, the observer recorded his or her personal values of Lth* (ath* or bth* ). Table 1 shows the values of the weight coefficients [9] obtained by averaging the estimates from 20 observers. The experimental results showed that, as compared to other equal-contrast systems [11], the value of the th threshold contrast K LAB deviates slightly from one when the color of fine details and brightness of the background change. Thus, the N-CIELAB system is more adequate for identification of fine details.

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3. ALGORITHM FOR IDENTIFICATION OF FINE DETAILS Let us consider an algorithm for identification of the finest details (δ = 1) in an image. 1. Transform the image from RGB into L*a*b* pixel by pixel. 2. Select the first microblock by using a mask 2 × 2 pixels in size, assign it a number m = 0, and evaluate its contrast:

K m = (ΔLm* /Lth* )2 + (Δam*/ath*)2 + (Δbm*/bth*)2 ,

(5)

where ΔLm* , Δam* , and Δbm* are the mean deviations of the color coordinates in the microblock m: N −1

∑

* , ΔLm* = 1 Ln* − Lmean N n=0

N −1

∑

* , Δam* = 1 an* − amean N n=0 (6) N −1 1 * ; Δbm* = bn* − bmean N n=0

∑

Lth* , ath* , and bth* are the weight coefficients from Table 1 for fine details one pixel in size; Ln*, an* , and bn* are * , amean * , and bmean * are the mean color coordinates; Lmean values of the color coordinates in the microblock; and N is the number of pixels in the microblock (N = 4). 3. Check the condition K m > K th,

(7)

where K th is the threshold contrast for which neighbor pixels in the microblock become visually perceivable. 4. If condition (7) holds, then the microblock contains fine details; label it with a marker b1(m) = 1, where m is the number of the microblock. 5. If condition (7) does not hold, then the microblock contains no visually perceivable fine details; label it with b1(m) = 0. 6. Shift the coordinates of the mask by two pixels, go to the next microblock with a number m = 1, and repeat steps 2–5. Upon analyzing the whole image, we obtain a set of regions in the form of microblocks 2 × 2 pixels in size that are labeled by the marker b1(m) = 1. At the next stage, we identify fine details two pixels in size (δ = 2). 1. Select the first block by using a mask 4 × 4 pixels in size. 2. Check if there are any microblocks (2 × 2) with the marker b1 = 1. If there are no such microblocks in the block (4 × 4), then evaluate its contrast (5), where Lth* , ath* , and bth* are the weight coefficients from Table 1 for fine details with the size δ = 2 and N = 16 is the number of elements in the block. 3. Check condition (7). PATTERN RECOGNITION AND IMAGE ANALYSIS

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4. If condition (7) holds, then the block contains fine details with the size δ = 2; label it with a marker b2(m) = 2 and go to the next block. 5. If condition (7) does not hold or the block contains microblocks with the marker b1 = 1, label it with a marker b2(m) = 0 and go to the next block. Details 4 × 4 pixels in size are segmented similarly. Select blocks 8 × 8 pixels in size and check if there are microblocks with the markers b1 = 1 and b2 = 2. If there are no such microblocks, then evaluate the contrast of the block by using formula (5), where Lth* , ath* , and bth* are the weight coefficients from Table 1 for δ = 4. Check condition (7). If condition (7) holds, then label this block with a marker b4(m) = 4 and go to the next block. Once the microblocks 2 × 2, 4 × 4, and 8 × 8 pixels in size are identified, label the remaining blocks (8 × 8) with a marker bF(m) = F and classify them as background blocks with slight internal variations in color coordinates. Thus, upon executing the identification algorithm, we select, using the markers b1, b2, and b4, three sets of regions (microblocks) comprising fine details, as well as select background blocks by using the marker bF.

The threshold K th in formula (7) is chosen based on the following considerations. Metric (4) implies that if th the color contrast is K LAB = K LAB = 1, then the fine details of a test table become visually perceivable as individual objects on the homogeneous achromatic background. For real images, the visual contrast of a microblock decreases due to the inhomogeneity of the background and the masking effect of neighbor microblocks. In [7], some experiments were described that estimated the effect of the threshold K th on the identification of microblocks in photorealistic images. In that paper, for analysis of photorealistic images, it was proposed to use the value K th = 1.5 . Why did we choose the normalized system NCIELAB? The contrast of a microblock (5) can be evaluated in any other color coordinate system, for example [12] in RGB, YUV, HSI, WUV, Luv, etc. Based on many experiments, we can conclude that the N-CIELAB system has the following advantages: evaluation of contrast by Eqs. (5)–(7) corresponds to visual contrast sensitivity, does not depend on color, and depends only slightly on the brightness of the background. Thus, the proposed algorithm makes it possible to identify fragments of photorealistic images with fine details taking into account the contrast sensitivity of the human eye. In addition, the algorithm allows one to estimate the finest-detail level (FDL) [13]:

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4 ⋅ N b1 , W ⋅H

(8)

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where W and H are the width and height of an image and Nb1 is the number of microblocks with the marker b1. Experimental investigations [9, 10] of the algorithm showed that it yields adequate results when analyzing high-quality digital images. If an image is noisy, then a low peak signal-tonoise ratio (PSNR) leads to identification of noise components as fine details and FDL grows. Artifacts in the compression algorithms JPEG 2000 or JPEG with high compression level (low quality) cause FDL to decrease due to blurring of fine details. Based on the results of our experiments, we can conclude that the value of FDL changes slightly for PSNR > 40 dB and/or JPEG compression with highquality settings (80–100%). 4. CLASSIFICATION OF BLOCKS AND OPTIMIZATION OF QUANTIZATION PARAMETERS Consider classification of image blocks (8 × 8) for adaptive quantization of DCT coefficients in the JPEG compressor. At the first stage, microblocks with fine details are identified by the algorithm described above to obtain microblocks with the markers b1, b2, b4, and bF. At the second stage, blocks (8 × 8) are classified using the following algorithm: − in each block (8 × 8), search for microblocks with the markers b1, b2, and b4; − if the block contains at least one microblock with the marker b1, then label this block with the marker B1; − if the block does not contain microblocks with the marker b1 but contains at least one microblock with the marker b2, then label this block with the marker B2; − if the block does not contain microblocks with the markers b1 and b2 but contains a microblock with the marker b4, then label this block with the marker B4; − label the remaining blocks (which do not contain microblocks with the markers b1, b2, and b4) with the marker BF. Note that labeling requires two additional bits of information, which can be placed in the header of a block or, upon compression by the modified Huffman method, at the end of a compressed block [1]. Figure 1 shows a fragment (512 × 512) of a test image and selected fragments with blocks B1, B2, and B4. At the next stage, the quality parameter (Q) of the quantizer needs to be selected depending on the class of a block. For this purpose, a program model of the JPEG codec was developed in C++; it allows the parameter Q to be selected for each block class with respect to brightness (Y) and color (U, V). The program implements all JPEG functions [1]: RGB-to-YUV conversion using the color model 4 : 4 : 4; partitioning of the matrices Y, U, and V into blocks (8 × 8); execution of

block DCT; quantization of blocks with the parameter Q; zigzag scanning; and statistical compression. Blocks in the brightness and color matrices are quantized by the formulas

Yi, j,m =

Y QBk ⋅ Yi, j,m

M iY, j

U i, j,m =

;

Vi, j,m =

UV QBk ⋅ Vi, j,m

M iUV ,j

UV QBk ⋅ U i, j,m

M iUV ,j

;

,

where m is the number of a block, i and j are the spatial coordinates of the DCT coefficients within the block (8 × 8), MY and MUV are the brightness and color quantization matrices recommended by the JPEG Y UV standard, and QBk and QBk are the block quantization parameters that depend on the class of the block (B1, B2, B4, and BF). JPEG compression artifacts are known to significantly distort fine details, with these distortions being the most visible to the human eye [14]. That is why we propose a decreasing progression in the parameter Q depending on the class of a block, where its maximum value, Qmax, corresponds to the blocks with the marker B1. To evaluate the adaptive quantization parameters, we use the following technique. At the first stage, it is required to determine the maximum values of QBY1 and QBUV1 for which distortions in the blocks B1 are invisible or slightly visible. In the course of the experiment, the observer selects a test image and varies the parameter QBY1 from its minimum value to the value for which distortions become invisible. In this case, the other quantization parameters Y UV and QBUV1 , QBUV2 , QBUV4 , QBF are set to their QBY 2 , QBY 4 , QBF maximum values. To render distortions, the program uses a module that implements the following function. For each segmented block, it calculates deviations of the color coordinates in    ) images: the original (RGB) and distorted (RGB

ΔR1(i, j ) = R(i, j ) − R (i, j ); ΔG1(i, j ) = G (i, j ) − G (i, j ); ΔB1(i, j ) = B(i, j ) − B (i, j ).

(9)

Then the deviations of the color coordinates are rendered in the blocks B1 on the achromatic (gray) background with brightness YF. Figure 2 shows fragments of a test image and selected blocks B1 with distortions for different values of the parameter QBY1 . Upon determination of the maximum value QBY1 = 100%, the observer varies the value of the parameter

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(a)

75

(b)

B1

Original (c)

(d)

B2

B4 Fig. 1. Selecting the blocks of a test image.

QBUV1 in color blocks until distortions disappear. In this case, the parameter QBY1 is set to its maximum value. During experiments and analysis of some photorealistic images with different degrees of detail, we found the mean value of QBY1 for which distortions in blocks B1 become invisible (slightly visible) to the human eye and equated it to the maximum parameter: QBY1 = Qmax = 100%. To confirm the adequacy of the value for Qmax, we carried out similar experiments in the well-known applications Adobe Photoshop CS and ACD See Pro Photo Manager. In the course of these experiments, using the program module implementing function (9), the observer estimated visual distortions of a compressed image for different JPEG quality parameters. It was found that the visual estimates of distortions based on the invisibility (slight visibility) criterion correspond to the following: for the developed program, PATTERN RECOGNITION AND IMAGE ANALYSIS

QBY1 = Qmax; for Adobe Photoshop CS, Quality = 12 (the maximum value of the quality parameter); and for ACD See Pro Photo Manager, Quality = 100% (the maximum value of the quality parameter). We also experimentally found the numerical value of the quantization parameter for color blocks QBUV1 , which was 70% of Qmax. For quantitative estimation of distortions in segmented blocks B1, B2, B4, and BF, the mean brightness (L*) and color (a*, b*) deviations of color coordinates in the CIELAB system are calculated using the metric

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L εBk = 1 M Bk ab εBk

No. 1

= 1 M Bk 2018

M Bk −1

∑

m =0

M Bk −1

∑

m =0

ΔLm* ; (10)

(Δam*) + (Δbm*) , 2

2

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(a)

(b)

Y QB1 = 100%

(c)

Y QB1 = 50%

Y QB1 = 10%

Fig. 2. Rendering the distortions in blocks B1.

where MBk is the number of selected blocks with the marker Bk; k is a parameter that indicates the type of a microblock (B1, B2, B4, or BF); ΔLm*, Δam* , and Δbm* are the mean deviations of color coordinates in the block with the number m for the original and decoded images:

ΔLm* = 1 N

N −1

∑ n=0

L*n,m − L*n,m , * Δbm = 1 N

Δam* = 1 N

N −1

∑ b* n=0

n,m

N −1

∑ a* n=0

− bn*,m ;

n,m

− an*,m , (11)

L*n,m , an*,m, and bn*,m are the color coordinates for the original image; L*n,m , an*,m, and bn*,m are the color coordinates for the decoded image; and N is the number of pixels in a block (N = 64). Note that, in contrast to (5), expression (10) does * , a *, and b *. This is not use the weight coefficients Lth th th due to the following. 1. Expression (5) evaluates the visual contrast of fine details (in a microblock) for which they can be perceived by the human eye. Metric (10) evaluates deviations of color coordinates for the original and decoded images, i.e., distortions of a block rather than its contrast. 2. Blocks (8 × 8) can contain fine details from different microblocks. For instance, in addition to microblocks b1, blocks with the marker B1 may contain microblocks b2, which complicates the selection of the weight coefficients. Thus, in contrast to the well-known distortion metrics (MSE, PSNR, SSIM, etc.) [15, 16], we have four brightness-distortion parameters (εLB1, εLB 2 , εLB 4 ,

and εLBF ) and four color-distortion parameters (εab B1, ab ab ab εB 2 , εB 4 , and εBF ) for four classes of image blocks. The values of these parameters correspond to visual estimates of color differences between the original and distorted images. Table 2 shows the distortion parameters for the fragments (512 × 512) of three test images (see Fig. 3), where the same value Qmax = 100% is set for all blocks; the table also shows the values of the compression ratio and PSNR (dB). Analysis of distorted test images shows the following. 1. For Q = 100%, distortions in segmented regions are tolerable and almost invisible to the human eye.

2. Brightness distortions εLBk differ slightly for different types of images. 3. Color distortions εab Bk depend on saturation (S) and increase for test images with a higher value of S, e.g., for Image 2. 4. Distortions are maximal in blocks B1 (with the finest details) and decrease in blocks B2, B4, and BF, which allows us to use adaptive quantization. Upon processing of a number of photorealistic test images, we found that, on average, distortions in blocks B2 are almost invisible to the human eye if the quantization parameter QBY 2 exceeds 70%. To determine the quantization parameter QBUV2 for color, the observer varied its value at QBY 2 = 70%. The quantization parameters in blocks B4 and BF were estimated similarly. The experimental results suggest that high visual quality of images is ensured for the following parameters of quantization in segmented regions of images:

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QB1 = 100%;

QB 2 = 70%;

QB 4 = 50%;

QBF = 30%;

Y

Y

Table 2. Estimates of JPEG distortions (Q = 100%)

Y

Y

(12)

for brightness blocks (Y) and

QBUV1 QBUV4

= 70%; = 30%;

QBUV2 UV QBF

= 10%;

(13) Image 2

for color blocks (U, V). Table 3 shows the results of analyzing three test images (see Fig. 2) after JPEG compression with quantization parameters (12) and (13). Analysis of Tables 2 and 3 shows that, with our parameters, the use of adaptive quantization improves the compression ratio by 38% (Image 1), 16% (Image 2), and 29% (Image 3) while preserving high visual quality of the images. According to Table 3, with our adaptive quantization parameters, brightness distortions εLBk do not exceed an average of 0.5 and color distortions εab Bk do not exceed 1.5: L εBk ≤ 0.5;

ε Image 1

= 50%;

ab εBk ≤ 1.5 .

77

Image 3

(a)

Image 1

B4

BF PSNR compress

εLBk

0.39 0.38 0.33 0.29

εab Bk

1.32 1.16 1.02 0.90

εLBk

0.43 0.37 0.33 0.28

εab Bk

1.54 1.32 1.05 0.92

εLBk

0.41 0.39 0.31 0.27

εab Bk

0.89 0.78 0.56 0.43

ε Image 1

Image 2

Image 3

B1

B2

B4

BF

εLBk

0.39 0.46 0.44 0.39

εab Bk

1.57 1.43 1.22 1.09

εLBk

0.43 0.48 0.45 0.31

εab Bk

2.03 2.05 1.61 1.05

εLBk

0.41 0.50 0.42 0.36

εab Bk

0.99 0.93 0.74 0.66

43.8

4.25

42.7

2.18

44.9

4.47

PSNR compress 42.9

5.86

41.7

2.53

44.1

5.75

(in the same application) is ensured for the compression ratio not exceeding 3. Thus, using criteria (14), we can tune the compression ratio of JPEG 2000 for any class of images to ensure their high visual quality. 4. CONCLUSIONS In this paper, we have proposed a new image-compression algorithm that involves the following steps.

(b)

(c)

Image 2

Image 3

Fig. 3. Examples of test images. PATTERN RECOGNITION AND IMAGE ANALYSIS

B2

Table 3. Estimates of adaptive JPEG distortions

(14)

When analyzing more than 100 photorealistic images with different degrees of detail and saturation, we find that conditions (14) hold almost for all of the images. Thus, conditions (14) are valid criteria for high quality of images as they correspond to visual estimates and, therefore, can be used for parameter tuning of any other compression algorithms. As an example, Table 4 shows the results of analyzing Image 1 compressed with JPEG 2000 for different compression parameters. For compression, we used ACD See Pro Photo Manager. These results suggest that the high quality of this image is ensured for the compression ratio not exceeding 8. For comparison, the high quality of Image 2

B1

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Table 4. Estimates of JPEG 2000 distortions (Image 1)

ε

B1

B2

B4

BF

εLBk

0.26

0.29

0.29

0.28

εab Bk

1.08

1.08

1.02

0.94

εLBk

0.37

0.39

0.39

0.36

εab Bk

1.56

1.46

1.25

1.06

εLBk

0.55

0.58

0.53

0.42

εab Bk

1.93

1.69

1.35

1.11

εLBk

0.71

0.72

0.63

0.46

εab Bk

2.21

1.84

1.39

1.13

PSNR compress 44.3

5

tion,” Int. J. Emerging Technol. Adva. Eng. 4 (2) (2014). 6. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice Hall, 2008). 7. M. D. Fairchild, Color Appearance Models (John Wiley and Sons, 2005).

43.1

10

42.3

15

41.8

20

1. Segment the image to obtain four sets of microblocks (b1, b2, b4, and bF) depending on the contrast and size of fine details in the N-CIELAB normalized system. 2. Divide the blocks (8 × 8) into classes B1, B2, B4, and BF. 3. Run the JPEG algorithm with adaptive brightness and color quantization of blocks depending on their class. Based on the proposed distortion metric (10), we have derived valid criteria (14) of high image quality. The proposed algorithm has been experimentally shown to improve the compression of photorealistic images by 30% on average while preserving their high visual quality. We believe that the use of this algorithm in image compression systems can provide a tradeoff between compression ratio and quality. It should also be noted that the proposed algorithm can be employed in multimedia systems for high-quality video transmission at the stage of keyframe (I) compression.

8. X. Zhang, D. Silverstein, J. Farrell, and B. Wandell, “Color image quality metric SCIELAB and its application on halftone texture visibility,” in Proc. IEEE Compcon’97 (San Jose, CA, 1997), pp. 44–48. 9. S. V. Sai, N. Yu. Sorokin, and A. G. Shoberg, “Segmentation of fine details in the CIELAB,” in Proc. 24th Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision. WSCG 2016 (Plzen, May 30–June 3, 2016), pp. 155–162. 10. S. V. Sai, “Fine-detail level of photorealistic images: application in the multimedia system,” in Proc. IEEE Int. Siberian Conf. on Control and Communications (SIBCON-2015) (Omsk, 2015). 11. G. Wyszecki, “Uniform color scales: CIE 1964 U*V*W* conversion of OSA committee selection,” J. Opt. Soc. Am. 65, 456–460 (1975). 12. D. B. Judd, Color in Business, Science and Industry (John Wiley & Sons, 1975). 13. S. V. Sai, “Image segmentation algorithm in the system focusing digital camera,” in Proc. SPIE, Vol. 10176: Fundamental and Applied Problems of Photonics: Selected Papers of APCOM’2016 (2016). 14. P. G. J. Barten, Contrast Sensitivity of the Human Eye and Its Effects on Image Quality (HV Press, Knegsel, 1999). 15. W. K. Pratt, Digital Image Processing (Wiley, 2001). 16. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing 13 (4), 600–612 (2004).

REFERENCES 1. W. B. Pennebaker and J. L. Mitchel, JPEG Still Image Data Compression Standard (Springer, New York, 1992). 2. G. K. Wallace, “The JPEG still picture compression standard,” IEEE Trans. Consumer Electron. 38 (1), 1– 67 (1992). 3. F. Ernawan, and S. H. Nugraini, “The optimal quantization matrices for JPEG image compression from psychovisual threshold,” J. Theor. Appl. Inf. Techn. 70 (3), 566–572 (2014). 4. B. Aruna and Ch. Ramesh “A method for signaling block-adaptive quantization in baseline sequential JPEG,” Int. J. Modern Eng. Res. 2 (4), 2829–2831 (2012). 5. G. Gowripushpa, G. Santoshi, B. Ravikiran, J. Sharmila Rani, and K. Sri Harsha, “Implementation of ROI based baseline sequential adaptive quantiza-

Translated by Yu. Kornienko

Sergei Vladimirovich Sai. Born 1960. Graduated from the Tomsk University of Control Systems and Radioelectronics in 1983. Received candidate’s degree in 1990 and doctoral degree in 2003. Head of the Department of Computer Science at the Pacific National University. Scientific interests: digital processing, analysis, and recognition of images. Author of 98 papers, including 3 monographs and 16 papers in journals indexed in Scopus and Web of Science.

PATTERN RECOGNITION AND IMAGE ANALYSIS

Vol. 28

No. 1

2018