A Fast Noniterative Visually Lossless Compression of Dental Images Using BPG Coder

Kryvenko, Liudmyla; Krylova, O.; Krivenko, Sergey

doi:10.1109/meco55406.2022.9797114

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…First, the results on the distortion visibility threshold presented for images in the database TID2013 (Ponomarenko et al, 2015a) have been taken into account. Second, we have taken into account the results of verification experiments earlier carried out for the ADCT, AGU-M, and BPG Advanced Optical Technologies frontiersin.org coders with the fixed PCCs described in our papers (Krivenko et al, 2020;Krylova et al, 2021;Kryvenko et al, 2022). However, the threshold of distortion invisibility according to the PSNR-HVS-M metric is approximate and this has been confirmed by experiments in Bondžulić et al (2021).…”

Section: Figurementioning

confidence: 60%

“…Finally, for the BPG encoder, the results are the following (Kryvenko et al, 2022). It was recommended to set Q = 28 (analysis of several dependencies for particular image fragments presented in Figure 4 shows that this is a correct decision).…”

Section: The Use Of Fixed Pcc For Dct-based Codersmentioning

confidence: 99%

“…In particular, in the paper (Kryvenko et al, 2022), it was proposed to use the following procedure -apply compression with Q = 28 at the first step and determine PSNR-HVS-M 1 after decompression. If PSNR-HVS-M 1 is within the limits from 41.75 dB to 43.25 dB, leave the compression result.…”

Section: The Use Of Fixed Pcc For Dct-based Codersmentioning

confidence: 99%

“…Both approaches will be further analyzed and discussed in the remainder part of this paper. The latter one has been intensively studied in our recent papers (Krivenko et al, 2020;Krylova et al, 2021;Kryvenko et al, 2022) for three different DCT-based coders, namely, ADCTC (advanced DCT coder) (Ponomarenko et al, 2007), AGU-M (Zemliachenko et al, 2016), and better portable graphics (BPG) (BPG Image format, 2022) encoders, respectively. It has been shown that by setting a proper PCC [QS for the ADCTC, scaling factor (SF) for the AGU-M coder, and parameter Q for the BPG encoder] it is possible to provide the metric PSNR-HVS-M (Ponomarenko et al) in the range 40 .…”

Section: Introductionmentioning

confidence: 99%

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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: Figurementioning

confidence: 60%

Section: The Use Of Fixed Pcc For Dct-based Codersmentioning

confidence: 99%

Section: The Use Of Fixed Pcc For Dct-based Codersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent visually lossless compression of dental images

Kryvenko,

Krylova,

Lukin

et al. 2024

Adv. Opt. Technol.

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Performance Analysis of Recent Algorithms for Compression of Various Medical Images

Negi,

Garg,

Agrawal

2024

Communications in Computer and Information Science

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Lossless Medical Image Compression by Using Difference Transform

Rojas-Hernández

Díaz-de-León-Santiago

Barceló-Alonso

et al. 2022

Entropy

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This paper introduces a new method of compressing digital images by using the Difference Transform applied in medical imaging. The Difference Transform algorithm performs the decorrelation process of image data, and in this way improves the encoding process, achieving a file with a smaller size than the original. The proposed method proves to be competitive and in many cases better than the standards used for medical images such as TIFF or PNG. In addition, the Difference Transform can replace other transforms like Cosine or Wavelet.

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A Fast Noniterative Visually Lossless Compression of Dental Images Using BPG Coder

Cited by 4 publications

References 16 publications

Intelligent visually lossless compression of dental images

Intelligent visually lossless compression of dental images

Performance Analysis of Recent Algorithms for Compression of Various Medical Images

Lossless Medical Image Compression by Using Difference Transform

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