A video compression artifact reduction approach combined with quantization parameters estimation

Shuai, Xin; Qing, Linbo; Zhang, Mozhi; Sun, Weiheng; He, Xiaohai

doi:10.1007/s11227-022-04412-8

“…Color Noise: In color images, noise can affect individual color channels [20], leading to color distortion [21] or artifacts. Color noise may result from sensor limitations, compression artifacts [22], or other factors. Denoising color noise often requires specialized algorithms that consider the correlation between color channels [23].…”

Section: Noise Typesmentioning

confidence: 99%

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

Wu

2023

Preprint

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This paper explores the application of wavelet analysis, a multiscale approach for enhancing visual clarity, in the context of image denoising, which reduces the impact of noisy pixels, caused by various factors such as electronic sensor limitations, low-light conditions, or transmission errors in digital imaging systems. This paper introduces some common noise types like Gaussian noise and processes of image denoising, eliciting the strength of wavelet analysis. As a powerful image denoising technology, wavelet analysis needs five steps to process the image, and the key step is thresholding. There are many kinds of wavelet, and each wavelet has different advantages and functions, which makes it suitable for different applications. Hence, wavelet analysis also makes a contribution in various fields beyond signal processing and data analysis, such as Biomedical Imaging and Geophysics and Seismology, where preserving image quality is essential for accurate analysis and interpretation. In short, this research highlights the promise of wavelet analysis, emphasizing the use of high- quality image data.

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“…Over the past few years, neural network-based video coding has gained significant attention within video coding standards due to its exceptional performance. In particular, the video coding modules including intra/inter prediction, and in-loop filtering have been enhanced with diverse neural networks [20][21][22][23][24][25][26]. For inter prediction, an efficient scheme was introduced by leveraging the deep virtual reference frame (VRF) to enhance the reference for temporal redundancy removal in video coding [20].…”

Section: Introductionmentioning

confidence: 99%

“…Zhu et al [23] considered intra prediction as image colorization and proposed a generative adversarial networkbased method to remove spatial redundancy intelligently. For post-processing, Shuai et al [26] proposed a quality enhancement network with quantization parameters estimation to reduce the compression artifact. Overall, most of the existing studies have focused on optimizing traditional algorithms or neural networks for video coding standards, and there are several studies on frameworks combining neural networks with traditional coding frameworks.…”

Section: Introductionmentioning

confidence: 99%

Neural network-based cross-channel chroma prediction for versatile video coding

Liang,

Zhang

2024

J Supercomput

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Despite linear models being introduced in the latest Versatile Video Coding (VVC) standard to exploit the correlation among luma and chroma channels for removing redundancy, these models cannot take into account the nonlinearity of components, resulting in degraded intra prediction precision. In this paper, a neural network-based method is proposed for cross-channel chroma intra prediction to enhance the coding efficiency. Specifically, the neighboring reference and co-located samples are separately input into the proposed network to exploit spatial and cross-channel correlations fully. Furthermore, in order to acquire a more compact representation of residual signals, a transform-based loss is employed to enhance the effectiveness of the compression. The proposed method is integrated into VVC, competing with the intrinsic chroma prediction regarding rate-distortion optimization to enhance coding performance further. The extensive experimental results demonstrate the superiority of the proposed method over the VVC test model (VTM) 7.0, achieving average bitrate savings of 2.68% and 2.11% for U and V chroma components, respectively.

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Neural Network-Based Cross-Channel Chroma Prediction for Versatile Video Coding

Liang,

Zhang

2023

Preprint

0

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Despite linear models being introduced in the latest Versatile Video Coding (VVC) standard to exploit the correlation among luma and chroma channels for removing redundancy, these models cannot take into account the nonlinearity of components, resulting in degraded intra prediction precision. In this paper, a neural network-based method is proposed for cross-channel chroma intra prediction to enhance the coding efficiency. Specifically, the neighboring reference and co-located samples are separately input into the proposed network to exploit spatial and cross-channel correlations fully. Furthermore, in order to acquire a more compact representation of residual signals, a transform-based loss is employed to enhance the effectiveness of the compression. The proposed method is integrated into VVC, competing with the intrinsic chroma prediction regarding rate-distortion optimization to enhance coding performance further. The extensive experimental results demonstrate the superiority of the proposed method over the VVC test model (VTM) 7.0, achieving average bitrate savings of 2.68% and 2.11% for U and V chroma components, respectively.

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A video compression artifact reduction approach combined with quantization parameters estimation

Cited by 3 publications

References 37 publications

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

Wavelet Analysis for Image Denoising: A Multiscale Approach for Enhancing Visual Clarity

Neural network-based cross-channel chroma prediction for versatile video coding

Neural Network-Based Cross-Channel Chroma Prediction for Versatile Video Coding

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