文献类型：期刊文献

英文题名：BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment

作者：Chen, Zijian[1];Sun, Wei[1];Jia, Jun[1];Lu, Fangfang[2];Zhang, Zicheng[1];Liu, Jing[3];Huang, Ru[4];Min, Xiongkuo[1];Zhai, Guangtao[1]

机构：[1]Shanghai Jiao Tong Univ, Inst Image Commun & Informat Proc, Shanghai 200240, Peoples R China;[2]Shanghai Univ Elect Power, Coll Comp Sci & Technol, Shanghai 200290, Peoples R China;[3]Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China;[4]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：34

期号：7

起止页码：6347

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

收录：;EI(收录号：20240815586807);WOS:【SCI-EXPANDED(收录号:WOS:001263608800078)】；

基金：This work was supported in part by the China Postdoctoral Science Foundation under Grant 2023TQ0212 and Grant 2023M742298; in part by the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF) under Grant GZC20231618; in part by the Shanghai Pujiang Program under Grant 22PJ1407400; and in part by the National Natural Science Foundation of China under Grant 62271312, Grant 62301316, Grant 62101325, and Grant 62101326. This article was recommended by Associate Editor J. Liu.

语种：英文

外文关键词：Image quality assessment; banding artifact; frequency maps; database; dual-branch

摘要：Banding, also known as staircase-like contours, frequently occurs in flat areas of images/videos processed by compression or quantization algorithms. As undesirable artifacts, banding destroys the original image structure, thus inevitably degrading users' quality of experience (QoE). In this paper, we systematically investigate the banding image quality assessment (IQA) problem, aiming to detect the image banding artifacts and evaluate their perceptual visual quality. Considering that the existing image banding databases only contain limited content sources and banding generation methods, and lack perceptual quality labels (i.e. mean opinion scores), we first build the largest banding IQA database so far, named B anding A rtifact N oticeable D atabase (BAND-2k), which consists of 2,000 banding images generated by 15 compression and quantization schemes. A total of 23 workers participated in the subjective IQA experiment, yielding over 214,000 patch-level banding class labels and 44,371 reliable image-level quality rating scores. Subsequently, we develop an effective no-reference (NR) banding evaluator for banding detection and quality assessment by leveraging frequency characteristics of banding artifacts. To be more specific, a dual convolutional neural network (CNN) is employed to concurrently learn the feature representation from the high-frequency and low-frequency maps, thereby enhancing the ability to discern banding artifacts. The quality score of a banding image is generated by pooling the banding detection maps masked by the spatial frequency filters. The experimental results demonstrate that our banding evaluator achieves remarkably high accuracy in banding detection and also exhibits high SRCC and PLCC results with the perceptual quality labels, even without directly learning a regression model for banding quality evaluation. These findings unveil the strong correlations between the intensity of banding artifacts and the perceptual visual quality, thus validating the necessity of banding quality assessment. The BAND-2k database and the proposed banding evaluator are available at https://github.com/zijianchen98/ BAND-2k.