A Frame-Level Constant Bit-Rate Control Using Recursive Bayesian Estimation for Versatile Video Coding

Hyun, Myung Han; Lee, Bumshik; Kim, Munchurl

doi:10.1109/access.2020.3046043

Cited by 17 publications

(3 citation statements)

References 42 publications

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“…where covZ i ,Z j is 1 For VVC, the works in [67], [70], [86] introduced some adjustments to λ-domain algorithms.…”

Section: B λ-Domain Methodsmentioning

confidence: 99%

“…As an alternative to traditional methods that regard the entire RC process as a deterministic process, some methods treat the variables and parameters of RC as random variables to re-examine the RC process. Hyun et al [67] observed that the inaccuracy of existing linear rate estimation models incurs a decline in RC performance. Thus, they adopted a method called recursive Bayesian estimation (RBE) to precisely estimate rates.…”

Section: A Implementations At the Frame Levelmentioning

confidence: 99%

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Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

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Video coding is a video compression technique that compresses the original video sequence to achieve a smaller archive file or a lower transmission bandwidth under constraints on the visual quality loss. Rate control (RC) plays a critical role in video coding. It can achieve stable stream output in practical applications, especially in real-time video applications such as video conferencing or game live streaming. Most RC algorithms directly or indirectly characterize the relationship between the bit rate (R) and quantization (Q) and then allocate bits for every coding unit to guarantee the global bit rate and video quality level. This paper comprehensively reviews the classic RC technologies used in international video standards of past generations, analyses the mathematical models and implementation mechanisms of various schemes, and compares the performance of recent state-of-the-art RC algorithms. Finally, we discuss future directions and new application areas for RC methods. We hope that this review can help support the development, implementation, and application of RC in new video coding standards.

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“…where covZ i ,Z j is 1 For VVC, the works in [67], [70], [86] introduced some adjustments to λ-domain algorithms.…”

Section: B λ-Domain Methodsmentioning

confidence: 99%

Section: A Implementations At the Frame Levelmentioning

confidence: 99%

Recent Advances in Rate Control: From Optimization to Implementation and Beyond

Wei¹,

Zhou²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…If the rate estimation is incorrect for a particular frame or a CTU, the overall rate control performance is degraded impacting the end-user QoE. Hence, the adoption of machine learning models, Bayesian estimation methods is prominent in recent rate controlling algorithms [139]. The use of Convolutional Neural Network (CNN) for extracting spatial and temporal saliency features, and perceptual priority-based QP selection algorithms are becoming popular within the video coding domain [140].…”

Section: A Impact Of Image/video Compression On Qoementioning

confidence: 99%

Measuring, Modeling and Integrating Time-Varying Video Quality in End-to-End Multimedia Service Delivery: A Review and Open Challenges

et al. 2022

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The multimedia delivery chain consists of multiple stages such as content preparation, content delivery via Over-The-Top delivery network and Internet Service Providers network. Within the multimedia service chain, each stage influences the Quality of Experience (QoE) of the end user. The objective of this work is to provide a comprehensive literature survey with future research challenges and opportunities in the field of time-varying video quality in multimedia service delivery. The contribution of this work is two fold: 1) Survey -we provide a review of state-of-the-art works for video quality models to quantify multiple artifacts into a single QoE metric, pooling strategies for global quality measurements, and Continuous Time-Varying Quality (CTVQ) models; 2) Future Challenges and Directions -we investigate ten major research challenges and future directions based on the state-of-the-art for QoE modelling, QoE-aware encoding/decoding and QoE monitoring/management of multimedia streaming in next-generation networks.

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