Compression ratio prediction in lossy compression of noisy images

Zemliachenko, Alexander; Abramov, Sergey; Vozel, Benôıt; Chehdi, Kacem

doi:10.1109/igarss.2015.7326574

Cited by 11 publications

(12 citation statements)

References 15 publications

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“…Then one has to find such parameter(s) and the function. To solve this task, we have exploited our earlier experience in predicting filtering efficiency [49] and compression ratio [18] by simple analysis of DCT statistics in 8x8 pixel blocks and regression analysis [50,51].…”

Section: An Approach To Providing Controlled Lossesmentioning

confidence: 99%

“…Here by "controlled" we mean several aspects. First, distortions have to be measured or estimated or predicted to ensure that they are not larger than allowed threshold according to a certain metric (criterion) [17,18]. Second, introduced distortions can be accurately measured only if compression and decompression are already done.…”

Section: Introductionmentioning

confidence: 99%

“…Second, introduced distortions can be accurately measured only if compression and decompression are already done. Then, if distortion level has to be changed, coder parameters have to be changed and metric calculation has to be done after next iteration of compression/decompression [18]. This is often impractical, especially on-board.…”

Section: Introductionmentioning

confidence: 99%

“…Lossy compression of images with taking into account noise type [27] and characteristics has been paid considerable attention [28][29][30]. Possible existence of optimal operation point (OOP) and its prediction have been claimed and studied [13,18]. Problems of CR prediction and its providing for coders based on discrete cosine transform (DCT) have been considered [18,31].…”

Section: Introductionmentioning

confidence: 99%

“…Possible existence of optimal operation point (OOP) and its prediction have been claimed and studied [13,18]. Problems of CR prediction and its providing for coders based on discrete cosine transform (DCT) have been considered [18,31]. Meanwhile, problems of prediction of compressed image quality and providing a desired quality have not been thoroughly analyzed yet.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Lossy Compression of Remote Sensing Images with Controllable Distortions

Zemliachenko¹,

Krivenko²,

Vozel³

et al. 2019

Satellite Information Classification and Interpretation

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In this chapter, approaches to provide a desired quality of remote sensing images compressed in a lossy manner are considered. It is shown that, under certain conditions, this can be done automatically and quickly using prediction of coder performance parameters. The main parameters (metrics) are mean square error (MSE) or peak signal-to-noise ratio (PSNR) of introduced losses (distortions) although prediction of other important metrics is also possible. Having such a prediction, it becomes possible to set a quantization step of a coder in a proper manner to provide distortions of a desired level or less without compression/decompression iterations for single-channel image. It is shown that this approach can be also exploited in three-dimensional (3D) compression of multichannel images to produce a larger compression ratio (CR) for the same or less introduced distortions as for component-wise compression of multichannel data. The proposed methods are verified for test and real life images.

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