A Subjective Comparison of AV1 and HEVC for Adaptive Video Streaming

Katsenou, Angeliki; Zhang, Fan; Afonso, Mariana; Bull, David

doi:10.1109/icip.2019.8803523

Cited by 31 publications

(16 citation statements)

References 13 publications

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“…Our work focused on the compression of dynamically acquired point clouds, so the MPEG cat3-frame dataset acquired by the Lidar sensors is used for testing. The quality of the reconstructed geometric information is evaluated by the D1 and D2 metrics set according to the CTCs, where D1 measures the point-to-point distortion and D2 measuring the point-to-plane distortion [4,18].…”

Section: Methodsmentioning

confidence: 99%

An Improved Coarse-To-Fine Motion Estimation Scheme For Lidar Point Cloud Geometry Compression

Jin

Shao

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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Light detection and ranging (LiDAR) plays a vital role in autonomous driving applications. Due to the enormous volume of point cloud data from LiDAR acquisition, LiDAR point cloud compression schemes are desirable under limited storage and transmission bandwidth. This paper proposes an improved coarse-to-fine motion estimation scheme to significantly reduce the spatial redundancy among LiDAR point clouds, combining registration-based global motion estimation and block-based local motion estimation. First, in the global motion estimation process, an efficient scheme is introduced to segment ground level and object level from original point clouds. Then we use an ICP-based registration method for extracted object point cloud to estimate global motion transformation. Second, an irregular prediction unit (PU) partition method is introduced in the block-based local motion estimation part, which provides more flexibility to estimate complex motions. Experimental results have shown significant gains of our proposed model over the state-of-theart test model (TMC13) of MPEG Geometry-based PCC (G-PCC) standard. For the dynamically acquired point clouds, average coding gains of 8.0% and 1.2% are obtained respectively in lossy and lossless geometric coding.

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Section: Methodsmentioning

confidence: 99%

An Improved Coarse-To-Fine Motion Estimation Scheme For Lidar Point Cloud Geometry Compression

Jin

Shao

et al. 2021

2021 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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“…In 2018, AV1 (AOMedia Video 1) [25] was released as a competitor to HEVC. AV1 was primarily based on Google's video codec VP9 [26] and has comparable performance to HEVC [27,28,29,30].…”

Section: Video Codingmentioning

confidence: 99%

Encoding in the Dark Grand Challenge: an Overview

Anantrasirichai

Zhang

Malyugina

et al. 2020

2020 IEEE International Conference on Multimedia &Amp; Expo Workshops (ICMEW)

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A big part of the video content we consume from video providers consists of genres featuring low-light aesthetics. Low light sequences have special characteristics, such as spatio-temporal varying acquisition noise and light flickering, that make the encoding process challenging. To deal with the spatio-temporal incoherent noise, higher bitrates are used to achieve high objective quality. Additionally, the quality assessment metrics and methods have not been designed, trained or tested for this type of content. This has inspired us to trigger research in that area and propose a Grand Challenge on encoding low-light video sequences. In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment. From this exploration, our results show that VVC already achieves a high performance compared to simply denoising the video source prior to encoding. Moreover, the quality of the video streams can be further improved by employing a post-processing image enhancement method.

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“…Although recent video coding standards such as High E ciency Video Coding (HEVC) (1), VP9 (2) and AV1 (3) have achieved impressive compression gains with significantly better rate-quality performance compared to their predecessors, they are all challenged by certain types of content, in particular complex dynamic textures (see examples in (4)). The Versatile Video Coding (VVC) (5) standard under development adopts a similar coding architecture and, while o ering overall coding gains, still exhibits the same limitations.…”

Section: Introductionmentioning

confidence: 99%