Smart Lossy Compression of Images Based on Distortion Prediction

Krivenko, Sergey; Krylova, O.; Батаева, Екатерина Викторовна

doi:10.1615/telecomradeng.v77.i17.40

Cited by 19 publications

(16 citation statements)

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“…Alongside differences in metrics' values for the same QS considered above for QS ≈ 20 (similar differences take place for QS > 20, compare the corresponding plots in Figure 1b,d,f), there are interesting observations for QS ≤ 10 (more generally, QS ≤ DR / 25). In this case, MSE ≈ QS 2 / 12 [20], PSNR exceeds 39 dB and PSNR-HVS-M is not smaller than PSNR and exceeds 45 dB. Thus, the introduced distortions are not visible and the desired PSNR des (or, respectively, MSE des ) can be easily provided by a proper setting of QS as QS ≈ (12MSE des ) 1/2 .…”

Section: Performance Criteria Of Lossy Compression and Their Preliminary Analysismentioning

confidence: 99%

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Lossy Compression of Multichannel Remote Sensing Images with Quality Control

et al. 2020

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Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given.

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Section: Performance Criteria Of Lossy Compression and Their Preliminary Analysismentioning

confidence: 99%

“…Lossy compression can provide considerably larger CR values [12,13,[15][16][17] but at the expense of introduced distortions. It is always a problem to reach an appropriate compromise between compressed image quality (characterized in many different ways) and CR [11][12][13][18][19][20]. There are several reasons behind this.…”

Section: Introductionmentioning

confidence: 99%

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

et al. 2020

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“…This can be achieved by the expense of distortions where larger distortions are introduced for larger CR values. A question is what is a reasonable trade-off between an attained CR and introduced distortions [13,[22][23][24][25] and how can it be reached?…”

Section: Introductionmentioning

confidence: 99%

“…Then, the use of efficient spectral and spatial decorrelation transforms is needed [17,23,24], combined with modern coding techniques applied to quantized transform coefficients. Spectral decorrelation and 3D compression allow exploiting spectral redundancy of multichannel data inherent for many types of images as, e.g., multispectral and hyperspectral [25], to increase CR [24]. In this paper, we consider threechannel images combined of visible range components of Sentinel-2 images [25].…”

Section: Introductionmentioning

confidence: 99%

Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control

et al. 2021

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Lossy compression of remote sensing data has found numerous applications. Several requirements are usually imposed on methods and algorithms to be used. A large compression ratio has to be provided, introduced distortions should not lead to sufficient reduction of classification accuracy, compression has to be realized quickly enough, etc. An additional requirement could be to provide privacy of compressed data. In this paper, we show that these requirements can be easily and effectively realized by compression based on discrete atomic transform (DAT). Three-channel remote sensing (RS) images that are part of multispectral data are used as examples. It is demonstrated that the quality of images compressed by DAT can be varied and controlled by setting maximal absolute deviation. This parameter also strictly relates to more traditional metrics as root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) that can be controlled. It is also shown that there are several variants of DAT having different depths. Their performances are compared from different viewpoints, and the recommendations of transform depth are given. Effects of lossy compression on three-channel image classification using the maximum likelihood (ML) approach are studied. It is shown that the total probability of correct classification remains almost the same for a wide range of distortions introduced by lossy compression, although some variations of correct classification probabilities take place for particular classes depending on peculiarities of feature distributions. Experiments are carried out for multispectral Sentinel images of different complexities.

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“…Thus, a compression can become iterative for finding a proper parameter that controls compression (PCC), for example, quality factor for JPEG, bitrate for JPEG2000 or Set partitioning in hierarchical trees (SPIHT), quantization step (QS) for discrete cosine transform (DCT)-based coders [24][25][26][27], etc. Another bottleneck is that such a compression might need extensive computational and time expenses, which is undesirable.…”

Section: Introductionmentioning

confidence: 99%

A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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A noniterative approach to the problem of visually lossless compression of dental images is proposed for an image coder based on the discrete cosine transform (DCT) and partition scheme optimization. This approach considers the following peculiarities of the problem. It is necessary to carry out lossy compression of dental images to achieve large compression ratios (CRs). Since dental images are viewed and analyzed by specialists, it is important to preserve useful diagnostic information preventing appearance of any visible artifacts due to lossy compression. At last, dental images may contain noise having complex statistical and spectral properties. In this paper, we have analyzed and utilized dependences of three quality metrics (Peak signal-to-noise ratio, PSNR; eak Signal-to-Noise Ratio using Human Visual System and Masking (PSNR-HVS-M); and feature similarity, FSIM) on the quantization step (QS), which controls a compression ratio for the so-called advanced DCT coder (ADCTC). The threshold values of distortion visibility for these metrics have been considered. Finally, the recent results on detectable changes in noise intensity have been incorporated in the QS setting. A visual comparison of original and compressed images allows to conclude that the introduced distortions are practically undetectable for the proposed approach; meanwhile, the provided CR lies within the interval.

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Smart Lossy Compression of Images Based on Distortion Prediction

Cited by 19 publications

References 0 publications

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control

A Fast Method of Visually Lossless Compression of Dental Images

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