用于JNQP预测的多类大规模压缩视频数据集

IF 3.1 4区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Visual Communication and Image Representation Pub Date : 2025-06-18 DOI:10.1016/j.jvcir.2025.104513

Zhun Li , Yuyang Wang , Lianmin Zhang , Hongkui Wang , Haibing Yin , Yong Chen , Wei Zhang

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引用次数: 0

摘要

为了进一步提高视频压缩性能，人们发布了大量压缩视频数据集来预测感知视频编码的刚可注意失真（JND）或刚可注意量化参数（JNQP）。然而，现有的压缩视频数据集无法满足适用于不同编解码器和编码模式的JNQP（或JND）的精确预测。因此，针对目前主流的视频编解码器，本文选取了50个源视频，分别使用H.265和H.266编解码器，采用全内（AI）、随机接入（RA）和低延迟（LD）编码模式，采用38个量化参数进行压缩。然后，50名测试者被要求评估每个源视频的三个感知质量水平的JNQP值。所有JNQP样本都经过了充分的处理，以满足不同编码模式下每个编解码器的JNQP预测要求。我们的数据集是第一个跨多种编解码器和编码模式进行JNQP预测的数据集，该数据集由ALR-Video命名，可以从https://github.com/903365130/ALR-Video下载。

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ALR-Video: A multi-class large-scale compressed video dataset for JNQP prediction

In order to further improve video compression, numerous compressed video datasets have been released to predict the just noticeable distortion (JND) or the just noticeable quantization parameter (JNQP) for perceptual video coding. However, existing compressed video datasets are unable to meet the precise prediction of JNQP (or JND) applicable to different codecs and coding modes. Thus, regarding the current mainstream video codecs, this paper selects 50 source videos and compresses them with H.265 and H.266 codecs with the all intra (AI), the random access (RA) and the low delay (LD) coding modes using 38 quantization parameters. Then, 50 testers are asked to evaluate the JNQP values for three perceptual quality levels for each source video. All JNQP samples have been fully processed to meet the requirement of JNQP prediction for each codec under different coding modes. Our dataset is the first one for JNQP prediction across multiple codecs and coding modes, which is named by ALR-Video and can be downloaded at https://github.com/903365130/ALR-Video.

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来源期刊

Journal of Visual Communication and Image Representation 工程技术-计算机：软件工程

CiteScore

5.40

自引率

11.50%

发文量

188

审稿时长

9.9 months

期刊介绍： The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.