Video quality enhancement via QP adaptation based on perceptual coding maps

Papadopoulos, Miltiadis Alexios; Rai, Yashas; Katsenou, Angeliki; Agrafiotis, Dimitris; Callet, Patrick Le; Bull, David

doi:10.1109/icip.2017.8296781

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…In H.264/AVC, HEVC and VVC, uniform quantization were adopted and meanwhile perceptual quantization matrix/table [9,77] was developed to give smaller quantization interval to sensitive coefficients. In [64], Papadopoulos et al adjusted the 𝑄𝑃 in quantization to assign bits for each MB to reflect relative importance of each MB. Similarly, Zhang et al [105] established the relationship between the masking features of the spatiotemporal domain and the quantization parameters, then, selected the local 𝑄𝑃 perceptually according to the characteristics of the video content.…”

Section: Perceptually Optimized Transform and Quantizationmentioning

confidence: 99%

A Survey on Perceptually Optimized Video Coding

Zhang¹,

Zhu²,

Jiang³

et al. 2021

Preprint

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Videos are developing in the trends of Ultra High Definition (UHD), High Frame Rate (HFR), High Dynamic Range (HDR), Wide Color Gammut (WCG) and high fidelity, which provide users with more realistic visual experiences. However, the amount of video data increases exponentially and requires high efficiency video compression for storage and network transmission. Perceptually optimized video coding aims to exploit visual redundancies in videos so as to maximize compression efficiency. In this paper, we present a systematic survey on the recent advances and challenges on perceptually optimized video coding. Firstly, we present problem formulation and framework of perceptually optimized video coding, which includes visual perception modelling, visual quality assessment and perception guided coding optimization. Secondly, the recent advances on visual factors, key computational visual models and quality assessment models are presented. Thirdly, we do systematic review on perceptual video coding optimizations from four key aspects, which includes perceptually optimized bit allocation, rate-distortion optimization, transform and quantization, filtering and enhancement. In each part, problem formulation, working flow, recent advances, advantages and challenges are presented. Fourthly, perceptual coding performance of latest coding standards and tools are experimentally analyzed. Finally, challenging issues and future opportunities on perceptual video coding are identified.CCS Concepts: • Computing methodologies → Image compression.

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Section: Perceptually Optimized Transform and Quantizationmentioning

confidence: 99%

A Survey on Perceptually Optimized Video Coding

Zhang¹,

Zhu²,

Jiang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Several researchers have presented approaches that enhance compression of textured content either via synthesis [3]- [5], by adopting different quantization strategies for textured areas, e.g. [6], or by proposing better local motion compensation techniques for those areas, e.g. [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…It has been also used to develop video quality assessment metrics and future video coding algorithms, e.g. [6], [8]. Although the BVI-Texture dataset satisfies modern video specifications, the small number of available sequences is not sufficient for an extensive analysis of texture properties.…”

Section: Introductionmentioning

confidence: 99%

BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment

Katsenou

Dimitrov

et al. 2021

IEEE Trans. Multimedia

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Highly textured video content is challenging to compress since the bit-rate to video quality trade-off is high and complex perceptual masking influences performance. Test datasets that cover a wide range of texture types are thus important for codec evaluation, but few exist. In order to study the properties of video texture, this paper introduces a Synthetic video Texture dataset (BVI-SynTex) that was generated using a Computer-Generated Imagery (CGI) environment. It contains 196 sequences clustered in three different texture types and offers the capability of being able to generate many versions of the same scene with different video parameters. It therefore provides a flexible basis for studying the influence of texture type and parameters on video compression and perceived video quality. A thorough validation and comparison of BVI-SynTex with similarly textured natural video content is performed. The comparisons show that BVI-SynTex exhibits a comparable coverage over the spatial and temporal domain and that it produces similar encoding statistics to real video datasets. A subset of the BVI-SynTex dataset was selected to perform a subjective evaluation of compression using the MPEG HEVC codec.The results show the impact of the content parameters to both the compression efficiency and the perceived quality. The publicly available BVI-SynTex dataset contains all source sequences, the objective and subjective analysis results, providing a valuable resource for the research community.

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A Survey on Perceptually Optimized Video Coding

et al. 2023

View full text Add to dashboard Cite

To provide users with more realistic visual experiences, videos are developing in the trends of Ultra High Definition (UHD), High Frame Rate (HFR), High Dynamic Range (HDR), Wide Color Gammut (WCG) and high clarity. However, the data amount of videos increases exponentially, which requires high efficiency video compression for storage and network transmission. Perceptually optimized video coding aims to maximize compression efficiency by exploiting visual redundancies. In this paper, we present a broad and systematic survey on perceptually optimized video coding. Firstly, we present problem formulation and framework of the perceptually optimized video coding, which includes visual perception modelling, visual quality assessment and perceptual video coding optimization. Secondly, recent advances on visual factors, computational perceptual models and quality assessment models are presented. Thirdly, we review perceptual video coding optimizations from four key aspects, including perceptually optimized bit allocation, rate-distortion optimization, transform and quantization, filtering and enhancement. In each part, problem formulation, working flow, recent advances, advantages and challenges are presented. Fourthly, perceptual coding performances of the latest coding standards and tools are experimentally analyzed. Finally, challenging issues and future opportunities are identified.

show abstract

Video quality enhancement via QP adaptation based on perceptual coding maps

Cited by 3 publications

References 22 publications

A Survey on Perceptually Optimized Video Coding

A Survey on Perceptually Optimized Video Coding

BVI-SynTex: A Synthetic Video Texture Dataset for Video Compression and Quality Assessment

A Survey on Perceptually Optimized Video Coding

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