000210290 001__ 210290
000210290 005__ 20190317000229.0
000210290 022__ $$a1687-5176
000210290 02470 $$2ISI$$a000365807300001
000210290 0247_ $$2doi$$a10.1186/s13640-015-0091-4
000210290 037__ $$aARTICLE
000210290 245__ $$aBenchmarking of objective quality metrics for HDR image quality assessment
000210290 269__ $$a2015
000210290 260__ $$bHindawi Publishing Corporation$$c2015
000210290 336__ $$aJournal Articles
000210290 520__ $$aRecent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metrics estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between two metrics. Results demonstrated that HDR-VDP-2 is the most reliable predictor of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content.
000210290 6531_ $$aImage quality assessment
000210290 6531_ $$aobjective metrics
000210290 6531_ $$aHigh Dynamic Range
000210290 6531_ $$aJPEG XT
000210290 700__ $$0245954$$g170391$$aHanhart, Philippe
000210290 700__ $$aBernardo, Marco
000210290 700__ $$aPereira, Manuela
000210290 700__ $$aPinheiro, Antonio
000210290 700__ $$0240223$$g105043$$aEbrahimi, Touradj
000210290 773__ $$j2015$$tEURASIP Journal on Image and Video Processing$$k39
000210290 8564_ $$uhttps://infoscience.epfl.ch/record/210290/files/s13640-015-0091-4.pdf$$zPublisher's version$$s3402087$$yPublisher's version
000210290 909C0 $$0252077$$pMMSPL
000210290 909CO $$particle$$ooai:infoscience.tind.io:210290$$qGLOBAL_SET$$pSTI
000210290 917Z8 $$x170391
000210290 917Z8 $$x170391
000210290 917Z8 $$x170391
000210290 917Z8 $$x170391
000210290 937__ $$aEPFL-ARTICLE-210290
000210290 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000210290 980__ $$aARTICLE