EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

Abstract

As the human vision is a definitive assessor of video quality, the expanded interest for no-reference subjective quality assessment (SQA) is focusing on a definitive goal of coordinating with human observation. However, the widely used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, expertise, domain knowledge, and other factors which may influence on actual assessment. In this paper, a no-reference SQA metric is devised by simply exploiting the nature of human eye browsing on videos and analyzing the associated quality correlation features. The high efficiency video coding (HEVC) reference test model is first employed to produce different forms of coded video quality which then displayed to a number of partakers. Their eye-tracker recorded spatiotemporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the quality assessment related to assorted features such as length pursuit, angle deflection, pupil deviation, and gaze interlude from recorded gaze trajectory. The content and resolution invariant operations are carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric-eye maneuver (EMAN). Tested results reveal that the quality evaluation carried out by the EMAN is comparatively better than MOS and structural similarity (SSIM) in terms of assessing different aspects of coded video quality for a wide range of single view video contents. For the free viewpoint video (FVV), where the reference frame is not available, the EMAN could also better distinguish different qualities compared to the MOS and SSIM.