Analysis of noisy image lossy compression by BPG using visual quality metrics

Коваленко, Богдан; Naumenko, V. V.; Krivenko, Sergey

doi:10.1109/atit54053.2021.9678575

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…Summarizing the presented results and the data represented in the papers (Kovalenko et al, 2021 andNaumenko et al, 2021), it is possible to state that OOP can be observed and, if this happens, it is reasonable to compress such images in OOP. This providing compressed image quality closer to noise-free image compared to the original image and ensuring a rather large CR (Fig.…”

Section: Used Criteria and Rate/distortion Curvessupporting

confidence: 54%

“…Examples of such dependencies for six test images (the images Fr02, Fr03, and Fr04 are of approximately the same complexity as the test image Fr01 in Fig. 1, c) taken from the paper (Kovalenko et al, 2021) As one can see, three main areas can be defined in these curves. The first is for Q < 9 where dependencies are nonlinear and quality of compressed images is characterized by very high values of PSNR c and PSNR-HVS-M c that mean that almost no distortions are introduced.…”

Section: Used Criteria and Rate/distortion Curvesmentioning

confidence: 98%

“…For example, JPEG2000 (Taubman et al, 2013) and SPIHT (Said et al, 1996) allow providing a desired CR (or bits per pixel (BPP)) with wide diversity of compressed image quality depending on its complexity . In turn, a modern better portable graphics (BPG) coder (Yee et al, 2017 andAlbalawi et al, 2016) uses the parameter Q that allows controlling peak signal-to-noise ratio (PSNR) between original and compressed images in wide limits (Kovalenko et al, 2021). This is a good quality attracting our attention just to this coder although PSNR is not an adequate metric in characterizing image quality (Zhai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Used Criteria and Rate/distortion Curvessupporting

confidence: 54%

Section: Used Criteria and Rate/distortion Curvesmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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

Stankevich

Krivenko

Naumenko

et al. 2022

UJRS

Self Cite

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“…__________ *E-mail: b.kovalenko@khai.edu It is worth stressing that compression should not be only intended on reaching this trade-off, but it also has to take into account the peculiarities of images and possible noise presence (Penna et al, 2007 andKovalenko et al, 2021). This is due to the fact that, in practice, it is hard to find images without noise (although this noise can be invisible for low intensities), especially in remote sensing (RS) images that are often corrupted by a rather intensive noise.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the potential efficiency of post-filtering noisy images after lossy compression

Коваленко

Rebrov

2023

UJRS

Self Cite

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An increase in the number of images and their average size is the general trend nowadays. This increase leads to certain problems with data storage and transfer via communication lines. A common way to solve this problem is to apply lossy compression that provides sufficiently larger compression ratios compared to lossless compression approaches. However, lossy compression has several peculiarities, especially if a compressed image is corrupted by quite intensive noise. First, a specific noise-filtering effect is observed. Second, an optimal operational point (OOP) might exist where the quality of a compressed image is closer to the corresponding noise-free image than the quality of the original image according to a chosen quality metric. In this case, it is worth compressing this image in the OOP or its closest neighborhood. These peculiarities have been earlier studied and their positive impact on image quality improvement has been demonstrated. Filtering of noisy images due to lossy compression is not perfect. Because of this, it is worth checking can additional quality improvement be reached using such an approach as post-filtering. In this study, we attempt to answer the questions: “is it worth to post-filter an image after lossy compression, especially in OOP’s neighborhood? And what benefit can it bring in the sense of image quality?”. The study is carried out for better portable graphics (BPG) coder and the DCT-based filter focusing mainly on one-component (grayscale) images. The quality of images is characterized by several metrics such as PSNR, PSNR-HVS-M, and FSIM. Possible image quality increasing via post-filtering is demonstrated and the recommendations for filter parameter setting are given.

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“…An OOP might also exist for different coders, based both on a discrete cosine transform (DCT) or wavelets (for example, JPEG2000) [32]. Recently, the possible existence of an OOP [33,34] has been demonstrated for the coder, BPG (better portable graphics} [35][36][37]. This compression technique has several advantages.…”

Section: Introductionmentioning

confidence: 99%

BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters

et al. 2022

Self Cite

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With a resolution improvement, the size of modern remote sensing images increases. This makes it desirable to compress them, mostly by using lossy compression techniques. Often the images to be compressed (or some component images of multichannel remote sensing data) are noisy. The lossy compression of such images has several peculiarities dealing with specific noise filtering effects and evaluation of the compression technique’s performance. In particular, an optimal operation point (OOP) may exist where quality of a compressed image is closer to the corresponding noise-free (true) image than the uncompressed (original, noisy) image quality, according to certain criterion (metrics). In such a case, it is reasonable to automatically compress an image under interest in the OOP neighborhood, but without having the true image at disposal in practice, it is impossible to accurately determine if the OOP does exist. Here we show that, by a simple and fast preliminary analysis and pre-training, it is possible to predict the OOPs existence and the metric values in it with appropriate accuracy. The study is carried out for a better portable graphics (BPG) coder for additive white Gaussian noise, focusing mainly on one-component (grayscale) images. The results allow for concluding that prediction is possible for an improvement (reduction) in the quality metrics of PSNR and PSNR-HVS-M. In turn, this allows for decision-making about the existence or absence of an OOP. If an OOP is absent, a more “careful” compression is recommended. Having such rules, it then becomes possible to carry out the compression automatically. Additionally, possible modifications for the cases of signal-dependent noise and the joint compression of three-component images are considered and the possible existence of an OOP for these cases is demonstrated.

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Analysis of noisy image lossy compression by BPG using visual quality metrics

Cited by 7 publications

References 18 publications

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

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

Analysis of the potential efficiency of post-filtering noisy images after lossy compression

BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters

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