Progressive DCT-based coder and its comparison to atomic function based image lossy compression

Makarichev, Viktor; Brysina, Iryna

doi:10.1109/tcset55632.2022.9766871

Cited by 3 publications

(2 citation statements)

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“…One important task is to choose encoder parameters such that compression is performed near the optimal operating point (OOP) [20,[23][24][25][26], which ensures that the decoded image is as close as possible to the original image based on the chosen criterion. The existence of the optimal operating point (OOP) has been demonstrated for various types of noise [20], compression methods based on discrete cosine transform (DCT) [27,28], and wavelets [22]. Different criteria such as mean-square error (MSE) and peak signal-to-noise ratio (PSNR) [21,24], as well as metrics that consider visual quality such as PSNR-HVS-M and MSSIM [29], can be used to determine the OOP.…”

Section: Introductionmentioning

confidence: 99%

BPG-based compression analysis of Poisson-noisy medical images

Naumenko,

Kovalenko,

Lukin

2023

reks

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The subject matter is lossy compression using the BPG encoder for medical images with varying levels of visual complexity, which are corrupted by Poisson noise. The goal of this study is to determine the optimal parameters for image compression and select the most suitable metric for identifying the optimal operational point. The tasks addressed include: selecting test images sized 512x512 in grayscale with varying degrees of visual complexity, encompassing visually intricate images rich in edges and textures, moderately complex images with edges and textures adjacent to homogeneous regions, and visually simple images primarily composed of homogeneous regions; establishing image quality evaluation metrics and assessing their performance across different encoder compression parameters; choosing one or multiple metrics that distinctly identify the position of the optimal operational point; and providing recommendations based on the attained results regarding the compression of medical images corrupted by Poisson noise using a BPG encoder, with the aim of maximizing the restored image’s quality resemblance to the original. The employed methods encompass image quality assessment techniques employing MSE, PSNR, MSSIM, and PSNR-HVS-M metrics, as well as software modeling in Python without using the built-in Poisson noise generator. The ensuing results indicate that optimal operational points (OOP) can be discerned for all these metrics when the compressed image quality surpasses that of the corresponding original image, accompanied by a sufficiently high compression ratio. Moreover, striking a suitable balance between the compression ratio and image quality leads to partial noise reduction without introducing notable distortions in the compressed image. This study underscores the significance of employing appropriate metrics for evaluating the quality of compressed medical images and provides insights into determining the compression parameter Q to attain the BPG encoder’s optimal operational point for specific images. Conclusions. The scientific novelty of the findings encompasses the following: 1) the capability of all metrics to determine the OOP for images of moderate visual complexity or those dominated by homogeneous areas; MSE and PSNR metrics demonstrating superior results for images rich in textures and edges; 2) the research highlights the dependency of Q in the OOP on the average image intensity, which can be reasonably established for a given image earmarked for compression based on our outcomes. The compression ratios for images compressed at the OOP are sufficiently high, further substantiating the rationale for compressing images in close proximity to the OOP.

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Section: Introductionmentioning

confidence: 99%

BPG-based compression analysis of Poisson-noisy medical images

Naumenko,

Kovalenko,

Lukin

2023

reks

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“…Generally, ( ) was introduced in [23]. In [24], it has been shown in terms of image compression efficiency that DAT is as useful as DCT, which is classic data processing tool [25]. An important advantage of DAT compared to DCT is computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Spatial Complexity Reduction in Remote Sensing Image Compression by Atomic Functions

Makarichev

Brysina

Vozel

2022

IEEE Geosci. Remote Sensing Lett.

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Remote sensing (RS) digital images have a great variety of applications in solving real-world problems. Modern sensors provide this type of data of a very high resolution, which, in combination with a great number of acquired images, makes a problem of compressing RS-images of particular importance. In this letter, discrete atomic compression (DAC) and a problem of its spatial complexity reduction are considered. This approach provides data compression and protection features in combination with such image representation that is ready for applying different artificial intelligence methods. For this reason, its application to image processing is relevant. Several modifications that provide reducing the spatial complexity of DAC are proposed, and their efficiency is analyzed. In particular, it is shown that, using a block splitting procedure, it is possible to get a significant decrease in additional memory expenses without DAC's efficiency degradation in terms of lossy image compression.

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Comparison of DAT with DCT in a Viewpoint of Current Image Processing and Analysis Trends

Makarichev

Brysina

2022

2022 12th International Conference on Dependable Systems, Services and Technologies (DESSERT)

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Progressive DCT-based coder and its comparison to atomic function based image lossy compression

Cited by 3 publications

References 20 publications

BPG-based compression analysis of Poisson-noisy medical images

BPG-based compression analysis of Poisson-noisy medical images

Spatial Complexity Reduction in Remote Sensing Image Compression by Atomic Functions

Comparison of DAT with DCT in a Viewpoint of Current Image Processing and Analysis Trends

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