Efficient HEVC Encoding to Meet Bitrate and PSNR Requirements Using Parametric Modeling

Singhadia, Ashish; Pati, Preeti Samhita; Singhal, Chetna; Chakrabarti, Indrajit

doi:10.1007/s00034-022-01987-8

Cited by 6 publications

(2 citation statements)

References 66 publications

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“…An objective evaluation can avoid the uncertainty of subjective vision. Therefore, we use the peak signal-to-noise ratio (PSNR) [49], Structure similarity index measure (SSIM) [50], and feature similarity index measure (FSIM) [51] to evaluate the image quality objectively. The PSNR refers to the ratio of the maximum possible power of the maximum achievable signal strength to the destructive noise power that impacts its representation accuracy.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

et al. 2023

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In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex γ-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the γ-norm to outliers. Secondly, we use the l1-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing.

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Section: Experimental Results and Analysismentioning

confidence: 99%

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

et al. 2023

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“…The proposal incurs a small bitstream overhead for transmitting the displacement information, which is offset by encoding efficiency gains. Singhadia et al [31] establish an empirical relationship of PSNR and bitrate with quantization parameter, through optimization algorithm to find the best quantization parameter to achieve the highest PSNR. However, this method cannot perform efficient encoding when transmission errors occur.…”

Section: Related Workmentioning

confidence: 99%

Video Error-Resilience Encoding and Decoding Based on Wyner-Ziv Framework for Underwater Transmission

Zhang

Feng

2022

Wireless Communications and Mobile Computing

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Limited by the characteristics of underwater acoustic channels, the video transmission applications targeting deep-sea detection and operation tasks are facing severe challenges such as network failure and high delay, resulting in loss of video details, color distortion, blurring, and even bit errors, which seriously affect decoding quality of the video transmission and reception. In order to solve the problems of deep-sea long-distance wireless communication, this paper proposes an improved Wyner-Ziv coding scheme (UnderWater-WZ) for video transmission through acoustic channels. The implementation process includes controlling error range by using MJPEG coding, combining motion compensation time interpolation with calibration information to generate high-quality side information. And intraframe quantization matrix is designed to weaken the change of video scene. The experimental results show that under the highest packet loss rate of 20%, this scheme can achieve 2.6~3.5 dB improvements in terms of video reconstruction compared to the previous methods, which is close to the error-free level.

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3D Cable Intelligent Management Platform Based on Parametric Modeling Technology

Luo,

Liu

2024

Lecture Notes in Electrical Engineering

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Efficient HEVC Encoding to Meet Bitrate and PSNR Requirements Using Parametric Modeling

Cited by 6 publications

References 66 publications

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

Video Error-Resilience Encoding and Decoding Based on Wyner-Ziv Framework for Underwater Transmission

3D Cable Intelligent Management Platform Based on Parametric Modeling Technology

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