A Survey on Perceptually Optimized Video Coding

Zhang, Yun; Zhu, Linwei; Jiang, Gangyi; Kuo, C.-C. Jay

doi:10.1145/3571727

Cited by 13 publications

(3 citation statements)

References 122 publications

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“…The experimental study evaluates the performance of deep learning-based video coding models, including STreamNet, DLIMD [18], and convLSTM [19], using five different datasets [20]: UCF10, HMDB51, Hollywood2, VID, and 20BN. The evaluation metrics include BPP, Bit Rate, PSNR [21], MS-SSIM [22], MSE, Compression Ratio, Encoding/Decoding Time, and BDBR [23].…”

Section: ░ 4 Experimental Studymentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

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Predicting future frames and improving inter-frame prediction are ongoing challenges in the field of video streaming. By creating a novel framework called STreamNet (Spatial-Temporal Video Coding), fusing bidirectional long short-term memory with temporal convolutional networks, this work aims to address the issue at hand. The development of STreamNet, which combines spatial hierarchies with local and global temporal dependencies in a seamless manner, along with sophisticated preprocessing, attention mechanisms, residual learning, and effective compression techniques, is the main contribution. Significantly, STreamNet claims to provide improved video coding quality and efficiency, making it suitable for next-generation networks. STreamNet has the potential to provide reliable and optimal streaming in high-demand network environments, as shown by preliminary tests that show a performance advantage over existing methods.

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Section: ░ 4 Experimental Studymentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

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“…Reducing the huge bitrate requirements of 8K video, which can reach up to 240 Mbps for raw uncompressed data (Zhang et al, 2023 To address these challenges and goals, several video coding standards have been developed and proposed by different organizations and groups. Among them, two of the most prominent and promising ones are High Efficiency Video Coding (HEVC) and AOMedia Video 1 (AV1).…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study of Video Coding Standards for 8K Ultra-High-Definition Video: HEVC vs. AV1

2023

AJETS

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This paper compares HEVC and AV1, two video coding standards, quality, complexity, and compatibility. Using five objective metrics and eight video sequences, the paper shows that AV1 generally outperforms HEVC in bitrate and quality, but also has higher complexity and lower compatibility. The paper discusses the findings and implications of the study, compares them with the existing literature, and provides some suggestions for future research or practice on 8K video coding.

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“…Video encoding employs a real-world link between consecutive edges, whereas picture reduction reduces apparent repetition in spatial areas. Employing learned video forecasts to replace classic block-based movement expectations and estimation algorithms is a significant component of profound learning-based video optimisation [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

Mudhavath,

Kumar,

Yadav

et al. 2024

Preprint

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The issues of finding a suitable loss function for perceived similarity and enhancing perceptual quality in substantially compressed videos still need to be resolved. The LIGAN-Conv-LSTM is a convolutional long-short-term memory system that integrates a loss-initialised generative adversarial network. This system was developed to address the challenge of defining unified training objectives that improve both rough and smooth content. The goal is to enhance compression quality by employing advanced feature selection and prediction techniques. The study begins by presenting a comprehensive examination of multimedia, emphasising its diverse attributes, encompassing retention, awareness, conversation, and knowledge. The research employs Shape Local Binary Texture (SLBT), Overlapped Segmentation-based Independent Component Analysis (OS-ICA), and Scale-Invariant Feature Transform (SIFT) as approaches to extract useful features. Furthermore, this research acknowledges the necessity for enhanced motion estimates. To address this, a hybrid technique is proposed to integrate the most advantageous elements of coyote and tuna swarm optimisation. The objective is to enhance the accessibility of feature selection, a crucial factor in improving classifier performance. The primary contribution of the research involves replacing standard motion estimating algorithms with a Conv-LSTM network, enabling the prediction of concise latent representations for future frames. The study's results emphasise the efficacy of the proposed hybrid search algorithm in achieving a high level of matching accuracy, as demonstrated by testing on a representative collection of video clips. The planned task's accuracy level is 97.73, demonstrating its cost-effective productivity.

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A Survey on Perceptually Optimized Video Coding

Cited by 13 publications

References 122 publications

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

A Comparative Study of Video Coding Standards for 8K Ultra-High-Definition Video: HEVC vs. AV1

A Loss-initiated GAN-based Convolutional LSTM Method for Compression and Motion Estimation-Based Objective Enhancement in Images and Videos

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