Usage of different Chroma Subsampling Modes in Image Compression by BPG Coder

Коваленко, Богдан

doi:10.36023/ujrs.2022.9.3.216

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…First, the format 4:4:4 provides slightly better quality of compressed images for Q = Q OOP = 33 and Q = 35 compared with the formats 4:2:2 and 4:2:0. Meanwhile, the format 4:2:0 is characterized by the largest CR (additional data supporting the same tendencies can be found in [60]). Thus, the decision of choosing the best format depends on the priority of the requirements.…”

Section: Comparison Of Image Compression For Different Formatssupporting

confidence: 64%

“…A thorough analysis has been carried out in [58] for the metrics PSNR-HVS-M [57] and MSSIM [59] for σ 2 = 100, and it has been demonstrated that the OOP (if it exists) takes place at Q OOP = 32 or 33. Additional analysis has been carried out in our paper [60] for the visual quality metrics PSNR-HA [20] and MDSI [19] for σ 2 = 100. It has been shown again that there are images for which the OOP exists (according to the aforementioned metrics) and this occurred at Q OOP = 33.…”

Section: Image/noise Model and Behavior Of Compression Efficiency Cri...mentioning

confidence: 99%

“…Meanwhile, in [60], for three-channel data, it is recommended to determine Q for the OOP as (see the data in Figures 3 and 4 for confirmation) Q OOP ≈ 12.9 + 20log 10 (σ).…”

Section: Image/noise Model and Behavior Of Compression Efficiency Cri...mentioning

confidence: 99%

“…Because of this, additional comparison and analysis are required. This will be undertaken in the next Section; additionally, some comparison results are given in [60] where it is shown that each format has its advantages and drawbacks.…”

Section: Prediction Of Oop Existence and Parameters In Itmentioning

confidence: 99%

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BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

Коваленко

Vozel

2023

Remote Sensing

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Nowadays, there is a clear trend toward increasing the number of remote-sensing images acquired and their average size. This leads to the need to compress the images for storage, dissemination, and transfer over communication lines where lossy compression techniques are more popular. The images to be compressed or some of their components are often noisy. They must therefore be compressed taking into account the properties of the noise. Due to the noise filtering effect obtained during lossy compression of noisy images, an optimal operating point (OOP) may exist. The OOP is a parameter that controls the compression for which the quality of the compressed image is closer (closest) to the corresponding noise-free image than the quality of the noisy (original, uncompressed) image according to some quantitative criterion (metric). In practice, it is important to know whether the OOP exists for a given image, because if the OOP exists, it is appropriate to perform the compression in the OOP or at least in its neighborhood. Since the real image is absent in practice, it is impossible to determine a priori whether the OOP exists or not. Here, we focus on three-channel-remote-sensing images and show that it is possible to easily predict the existence of the OOP. Furthermore, it is possible to predict the metric values or their improvements with appropriate accuracy for practical use. The BPG (better portable graphics) encoder is considered a special case of an efficient compression technique. As an initial design step, the case of additive white Gaussian noise with equal variance in the three components is considered. While previous research was mainly focused on predicting the improvement (reduction) of the PSNR and PSNR-HVS-M metrics, here we focus on the modern visual quality metrics, namely PSNR-HA and MDSI. We also discuss what to do if, according to the prediction, an OOP is absent. Examples of lossy compression of noisy three-channel remote sensing images are given. It is also shown that the use of three-dimensional compression provides a compression ratio increase by several times compared with component-wise compression in the OOP.

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Section: Comparison Of Image Compression For Different Formatssupporting

confidence: 64%

Section: Image/noise Model and Behavior Of Compression Efficiency Cri...mentioning

confidence: 99%

“…Meanwhile, in [60], for three-channel data, it is recommended to determine Q for the OOP as (see the data in Figures 3 and 4 for confirmation) Q OOP ≈ 12.9 + 20log 10 (σ).…”

Section: Image/noise Model and Behavior Of Compression Efficiency Cri...mentioning

confidence: 99%

Section: Prediction Of Oop Existence and Parameters In Itmentioning

confidence: 99%

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BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

Коваленко

Vozel

2023

Remote Sensing

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BPG-based compression of Poisson noisy images

Kovalenko,

Lukin

2023

2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT)

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Analysis of the potential efficiency of post-filtering noisy images after lossy compression

Коваленко

Rebrov

2023

UJRS

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An increase in the number of images and their average size is the general trend nowadays. This increase leads to certain problems with data storage and transfer via communication lines. A common way to solve this problem is to apply lossy compression that provides sufficiently larger compression ratios compared to lossless compression approaches. However, lossy compression has several peculiarities, especially if a compressed image is corrupted by quite intensive noise. First, a specific noise-filtering effect is observed. Second, an optimal operational point (OOP) might exist where the quality of a compressed image is closer to the corresponding noise-free image than the quality of the original image according to a chosen quality metric. In this case, it is worth compressing this image in the OOP or its closest neighborhood. These peculiarities have been earlier studied and their positive impact on image quality improvement has been demonstrated. Filtering of noisy images due to lossy compression is not perfect. Because of this, it is worth checking can additional quality improvement be reached using such an approach as post-filtering. In this study, we attempt to answer the questions: “is it worth to post-filter an image after lossy compression, especially in OOP’s neighborhood? And what benefit can it bring in the sense of image quality?”. The study is carried out for better portable graphics (BPG) coder and the DCT-based filter focusing mainly on one-component (grayscale) images. The quality of images is characterized by several metrics such as PSNR, PSNR-HVS-M, and FSIM. Possible image quality increasing via post-filtering is demonstrated and the recommendations for filter parameter setting are given.

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Usage of different Chroma Subsampling Modes in Image Compression by BPG Coder

Cited by 3 publications

References 12 publications

BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

BPG-Based Lossy Compression of Three-Channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters

BPG-based compression of Poisson noisy images

Analysis of the potential efficiency of post-filtering noisy images after lossy compression

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