Digital video quality metric based on human vision

Abstract

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (digital video quality) [A. B. Watson, in Human Vision, Visual Processing, and Digital Display VIII, Proc. SPIE 3299, 139-147 (1998)]. Here, we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the discrete cosine transform. It incorporates aspects of early visual processing, including light adaptation, luminance, and chromatic channels; spatial and temporal filtering; spatial frequency channels; contrast masking; and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers.

Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

“…Significant effort has also been applied to designing objective algorithms that are capable of predicting visual quality with high correlation against subjective perception [11]- [14]. Previous subjective studies on VQA have been performed on large format displays such as CRT/LCD monitors, while typically distorted videos have included compressed videos (H.264/MPEG), videos transmitted over wireless/IP channels [5], [6] and jittered and delayed videos [15], [16].…”

Combined Source and Channel Strategies for Optimized Video Communications

“…In the past, several multi-channel vision models have been developed by researchers, including Daly (1993) and Watson (2001), for example. These models generally integrate a more complete representation of significant visual phenomena, such as spatiotemporal masking or orientation selectivity.…”

Perceptual-based Quality Metrics for Image and Video Services: A Survey

“…Quality predictors utilising these descriptors are then proposed to quantify the quality degradation due to loss of the different frame types. The performance of the motion based measure has been extensively evaluated with respect to the full-reference metric in [26] which incorporates aspects of different levels of the HVS.…”