Reduced reference video quality assessment based on spatial HVS mutual masking and temporal motion estimation
In this paper, an effective reduced reference (RR) video quality assessment (VQA) is proposed by depicting both the spatial and temporal statistical characteristics of the video signals. For each video frame, spatial information change (SIC) is employed to depict the energy variation. A novel mutual masking strategy based on the extracted SIC is proposed to accurately simulate the human visual system (HVS) texture masking property. For adjacent video frames, the temporal relationship is depicted by block-based motion estimation (BME). The generalized Gaussian density (GGD) function is employed to depict the histogram natural statistic of the residual frame after BME. The city-block distance (CBD) is used to measure the distance between histograms of the original and distorted video sequence. By pooling the measurements from both spatial and temporal perspectives, an efficient RR VQA is constructed. With the evaluations on the public video quality database, the proposed RR VQA demonstrated to be more effective than the representative RR VQAs and even the full-reference (FR) VQAs, such as peak signal-to-noise ratio (PSNR) and structure similarity index (SSIM) in matching the subjective ratings. Furthermore, the proposed RR VQA demonstrated to be much more effective and efficient, requiring only a very small number of bits for the RR feature representation.