A Perception-Based Hybrid Model for Video Quality Assessment

It is known that the human visual system (HVS) employs independent processes (distortion detection and artifact perception-also often referred to as near-threshold and suprathreshold distortion perception) to assess video quality for various distortion levels. Visual masking effects also play an important role in video distortion perception, especially within spatial and temporal textures. In this paper, a novel perception-based hybrid model for video quality assessment is presented. This simulates the HVS perception process by adaptively combining noticeable distortion and blurring artifacts using an enhanced nonlinear model. Noticeable distortion is defined by thresholding absolute differences using spatial and temporal tolerance maps that characterize texture masking effects, and this makes a significant contribution to quality assessment when the quality of the distorted video is similar to that of the original video. Characterization of blurring artifacts, estimated by computing high frequency energy variations and weighted with motion speed, is found to further improve metric performance. This is especially true for low quality cases. All stages of our model exploit the orientation selectivity and shift invariance properties of the dual-tree complex wavelet transform. This not only helps to improve the performance but also offers the potential for new low complexity in-loop application. Our approach is evaluated on both the Video Quality Experts Group (VQEG) full reference television Phase I and the Laboratory for Image and Video Engineering (LIVE) video databases. The resulting overall performance is superior to the existing metrics, exhibiting statistically better or equivalent performance with significantly lower complexity.