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On the performance of objective quality metrics for lightfields

Journal Contribution - Journal Article

Lightfield (LF) technology has attained significant attention in recent years due to its capability to capture much richer textural and geometric information in the scene compared to the classical 2D representation. The resampling and compression operations on LFs often lead to visual quality degradation, thus, sophisticated visual quality assessment methods play a crucial role to ensure a pleasant viewing experience. To this end, it is necessary to examine the performance of quality assessment methods for LF contents. The paper provides a comprehensive study on the reliability of various objective algorithms for LF quality prediction. Three subjectively-annotated LF data sets were selected and an extensive quality estimation analysis has been conducted using several objective quality assessment methods. In total, 250 LFs (more than 48000 perspective images) were evaluated. The results were compared against human opinion scores using various correlation indices and their statistical significance. Next, a decision-making strategy was adopted to choose the most reliable quality metrics for evaluation of LFs and finally, a metric fusion framework was proposed to further improve the quality prediction accuracy. To best of our knowledge, the benchmark and the analytical methodologies used in this paper are the most comprehensive study on the objective quality assessment methods for LF application.

Journal: Signal Processing : Image Communication
ISSN: 0923-5965
Volume: 93
Publication year:2021
Keywords:Compression, Lightfield imaging, Mean opinion score, Objective quality metrics, Quality of experience
BOF-keylabel:yes
IOF-keylabel:yes
BOF-publication weight:1
Authors:Regional
Authors from:Government, Higher Education
Accessibility:Closed