Subjective quality evaluation of compressed digital compound images

• Published in: Journal of visual communication and image representation Vol. 26; pp. 105 - 114
• Main Authors: Yang, Huan, Fang, Yuming, Yuan, Yuan, Lin, Weisi
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2015
• Subjects: Assessments; Balancing; Compound image quality assessment database; Compressed; Compressing; Digital; Digital compound image; Image compression; Image quality; Paired Comparison; Quality assessment; Representations; Subjective database; Subjective quality evaluation; Subjective test; Visual; Digital compound image; Subjective quality evaluation; Paired Comparison; Compound image quality assessment database; Image compression; Quality assessment; Subjective database; Subjective test
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2014.11.001

•We build a new Compound Image Quality Assessment Database (CIQAD).•Subjective test is conducted to get the MOS values of images in CIQAD.•Five compression methods are compared with respect to the obtained MOS values.•We study the applicability of existing IQA metrics to evaluate quality of DCIs.•Objective quality evaluation metric tailored for distorted DCIs is much desired. Visual quality evaluation of compressed Digital Compound Images (DCIs) becomes important in many multi-device communication systems. In this paper, we study subjective quality evaluation for compressed DCIs and investigate whether existing Image Quality Assessment (IQA) metrics are effective to evaluate the visual quality of compressed DCIs. A new Compound Image Quality Assessment Database (CIQAD) is therefore constructed, including 24 reference and 576 compressed DCIs. The subjective scores of these DCIs are obtained via visual judgement of 62 subjects using Paired Comparison (PC) in which the Hodgerank decomposition is adopted to generate uncompleted but near balanced pairs. Fourteen state-of-the-art IQA metrics are adopted to assess quality of images in CIQAD, and experimental results indicate that the existing IQA methods are limited in evaluating visual quality of DCIs. Compression results of five coding methods are thus compared with respect to different quality metrics to illustrate the limitation.