Analysis of Noisy Image Lossy Compression by BPG

Naumenko, V. V.; Krivenko, Sergey

doi:10.1007/978-3-030-94259-5_71

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In particular, the system Morita (Diagnostic and Imaging Equipment, 2020) produces spatially correlated signal-dependent noise (Abramova et al, 2020) that is visible, especially in homogeneous image regions. Lossy compression of noisy images has several specific features (Al-Shaykh and Mersereau, 1998;Zemliachenko et al, 2015;Naumenko et al, 2022) including the so-called noise filtering effect. In our case, we do not need to have the noise filtering effect due to lossy compression appearing itself to full extent.…”

Section: Introductionmentioning

confidence: 99%

Intelligent visually lossless compression of dental images

Kryvenko,

Krylova,

Lukin

et al. 2024

Adv. Opt. Technol.

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Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio.Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on.Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear.Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists).Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.

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Section: Introductionmentioning

confidence: 99%

Intelligent visually lossless compression of dental images

Kryvenko,

Krylova,

Lukin

et al. 2024

Adv. Opt. Technol.

View full text Add to dashboard Cite

show abstract

“…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

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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

Cited by 2 publications

References 16 publications

Intelligent visually lossless compression of dental images

Intelligent visually lossless compression of dental images

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

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