Still image/video frame lossy compression providing a desired visual quality

Zemliachenko, Alexander; Ponomarenko, Nikolay N.; Егиазарян, Карен; Astola, Jaakko

doi:10.1007/s11045-015-0333-8

Cited by 49 publications

(36 citation statements)

References 18 publications

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“…Note that the partition scheme also depends on QS (partition schemes in Figure 2 are shown for two values of QS). ADCTC outperforms many compression techniques, even including those proposed recently [24]. The corresponding comparisons have been made earlier using standard optical test images.…”

Section: Basic Dependencesmentioning

confidence: 96%

“…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%

“…Considering all the aforementioned features of the considered problem of lossy compression of dental images avoiding an appearance of visible artifacts (without losing diagnostically important information), we analyze a possibility of applying an advanced DCT coder (ADCT) codec (ADCTC) [36] within the fast and effective procedure. The interest in ADCTC is explained by the following factors: (a) This compression technique allows adapting to the image content and; thus, providing better performance characteristics than JPEG, JPEG2000, SPIHT and other coders [24]; (b) since this compression method is not standardized, a certain degree of security (privacy) of compressed data can be ensured. The goal of this paper is to find such a PCC value for ADCTC that guarantees invisibility of introduced distortions and fast compression due to the absence of iterations.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Basic Dependencesmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast Method of Visually Lossless Compression of Dental Images

et al. 2020

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“…Prediction of mean square error (MSE) of introduced losses for JPEG has been done [33]. However, control and prediction of metric values for more advanced coders as AGU [34] and ADCT [35] that outperform JPEG considerably [36] were not developed till last 2 years. Since providing of a desired metric value using iterative (multiple) compression/decompression requires sufficient time and resources [36], it was decided to design a new approach without iterations [37].…”

Section: Introductionmentioning

confidence: 99%

“…However, control and prediction of metric values for more advanced coders as AGU [34] and ADCT [35] that outperform JPEG considerably [36] were not developed till last 2 years. Since providing of a desired metric value using iterative (multiple) compression/decompression requires sufficient time and resources [36], it was decided to design a new approach without iterations [37]. Later this approach has been further advanced [38][39][40], mainly for singlecomponent (grayscale) images in 8-bit representation and with taking into account possible presence of noise.…”

Section: Introductionmentioning

confidence: 99%

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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