Efficient Prediction of the First Just Noticeable Difference Point for JPEG Compressed Images

Bondžulić, Boban; Stojanović, Nenad; Petrović, Vladimir; Pavlović, Boban; Miličević, Zoran

doi:10.12700/aph.18.8.2021.8.11

Cited by 20 publications

(17 citation statements)

References 15 publications

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“…Lossless image compression techniques [8,9] do not introduce distortions into compressed data but compression ratio (CR) for them is usually too small and this restricts their application in practice. Then, one has to apply lossy compression that introduces distortions inevitably [10,11]. The questions that arise are what distortions can be considered appropriate, how to control them, what compression method to apply, and so on [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Advantages and Drawbacks of Two-Step Approach to Providing Desired Parameters in Lossy Image Compression

Li,

Abramov,

Dohtiev

et al. 2024

A.I.S.

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The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of the two-step approach to lossy compression, to analyze the reasons of drawbacks, and to put forward possible ways to get around these shortcomings. Methods used: linear approximation, numerical simulation, statistical analysis. Results obtained: 1) the considered approach main advantage is that, in most applications, it provides substantial improvement of accuracy of providing a desired value of a controlled compression parameter after the second step compared to the first step; 2) the approach is quite universal and can be applied for different coders and different parameters of lossy compression to be provided; 3) the main problems and limitations happen due to the use of linear approximation and essential difference in behavior of rate/distortion curves for images of different complexity; 4) there are ways to avoid the approach drawbacks that employ adaptation to image complexity and/or use certain restrictions at the second step. Conclusions: based on the results of the study, it is worth 1) considering more complex approximations of rate-distortion curves; 2) paying more attention to adequate and fast algorithms of characterizing image complexity before compression; 3) using quality metrics that have quasi-linear rate/distortion curves for a given coder.

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Section: Introductionmentioning

confidence: 99%

Advantages and Drawbacks of Two-Step Approach to Providing Desired Parameters in Lossy Image Compression

Li,

Abramov,

Dohtiev

et al. 2024

A.I.S.

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show abstract

“…There are lossless and lossy image compression techniques [4][5][6]. Visually lossless approaches are separately considered sometimes as well [7,8]. Lossless compression is often inappropriate since it produces too small compression ratio (CR).…”

Section: Introductionmentioning

confidence: 99%

“…Because of this, visually lossless and lossy compression techniques are widely used. They introduce inevitable distortions (losses) and an appropriate trade-off between the compressed image quality and CR has to be provided [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Then, one has to control introduced distortions that affect compressed image quality in one or another way. This can be done by iterative procedures like that one described in [13] or by predicting just noticeable distortions [8] or in some other way [14]. In any case, one directly or indirectly uses the so-called rate-distortion curves or their basic properties.…”

Section: Introductionmentioning

confidence: 99%

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On strange images with application to lossy image compression

Bondžulić

Bujaković

2022

reks

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Single and three-channel images are widely used in numerous applications. Due to the increasing volume of such data, they must be compressed where lossy compression offers more opportunities. Usually, it is supposed that, for a given image, a larger compression ratio leads to worse quality of the compressed image according to all quality metrics. This is true for most practical cases. However, it has been found recently that images are called “strange” for which a rate-distortion curve like dependence of the peak signal-to-noise ratio on the quality factor or quantization step, behaves non-monotonously. This might cause problems in the lossy compression of images. Thus, the basic subject of this paper are the factors that determine this phenomenon. The main among them are artificial origin of an image, possible presence of large homogeneous regions, specific behavior of image histograms. The main goal of this paper is to consider and explain the peculiarities of the lossy compression of strange images. The tasks of this paper are to provide definitions of strange images and to check whether non-monotonicity of rate-distortion curves occurs for different coders and metrics. One more task is to put ideas and methodology forward of further studies intended to detect strange images before their compression. The main result is that non-monotonous behavior can be observed for the same image for several quality metrics and coders. This means that not the coder but image properties determine the probability of an image to being strange. Moreover, both grayscale and color images can be strange, and both the natural scene and artificial images can be strange. This depends more on image properties than on image origin and number of channels. In particular, the percentage of pixels that belong to large homogeneous regions and image entropy play an important role. As conclusions, we outline possible directions of future research that, in the first order, relate to the analysis of images in large databases to establish parameters that show that a given image can be considered as strange.

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“…Concerning factor #1lossy compression should not result in too large degradation of image quality with respect to its visual perception by humans or classification or edge/object detection. The corresponding studies have been performed to predict just noticeable distortions (Bondžulić et al, 2021) or other measures of acceptable degradation of compressed images (Zabala et al, 2006 andKrivenko et al, 2011). Concerning factor #2different compression techniques employ different mechanisms of CR or compressed image quality variation and control.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Parameters in Optimal Operation Point for BPG-based Lossy Compression of Noisy Images

Stankevich

Krivenko

Naumenko

et al. 2022

UJRS

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Lossy compression of images corrupted by noise has several peculiarities. First, a specific noise filtering effect is observed. Second, optimal operation point (OOP) can be observed, i.e. such coder parameter (e.g., quantization step) value can exist that quality of compressed image calculated with respect to noise-free image can be better compared to quality of uncompressed (original noisy) image. If OOP exists, it is worth compressing a given image in OOP, if no, other recommendations on coder parameter setting are reasonable. Since noise-free image is not available in practice, it is not possible to determine does OOP exist and what is image quality in it. In this paper, we show that OOP existence for several quality metrics can be predicted quite easily and quickly for grayscale images corrupted by additive white Gaussian noise and compressed by better portable graphics (BPG) encoder. Such a prediction is based on analysis of statistics of discrete cosine transform (DCT) coefficients calculated for a limited number of 8x8 pixel blocks. A scatter-plot of metric improvement (reduction) depending upon these statistics is obtained in advance and prediction curve fitting is performed. Recommendations on encoder parameter setting for cases of OOP absence are given.

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Efficient Prediction of the First Just Noticeable Difference Point for JPEG Compressed Images

Cited by 20 publications

References 15 publications

Advantages and Drawbacks of Two-Step Approach to Providing Desired Parameters in Lossy Image Compression

Advantages and Drawbacks of Two-Step Approach to Providing Desired Parameters in Lossy Image Compression

On strange images with application to lossy image compression

Prediction of Parameters in Optimal Operation Point for BPG-based Lossy Compression of Noisy Images

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