A reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion
The reduced-reference (RR) approximation of a full-reference (FR) video quality assessment method is a convenient way to build evaluation metrics which are both intrinsically well correlated with human judgments and feasible to implement in a network scenario, without the need to explore the perceptual significance of new video features through mean opinion score tests. In this paper, we propose a RR approximation of the video structural similarity index (VSSIM), a FR metric which is known to be well descriptive of the video quality perceived by users. We focus on the visual degradation produced by channel transmission errors: first, at the encoder, a small set of salient structural video features is assembled and transmitted through the RR channel to the end-user; then, at the decoder the feature vector is combined with a fine-granularity, no-reference estimate of the channel-induced distortion to produce the VSSIM approximation. By uniformly quantizing the feature vector and compressing it using a context-adaptive, variable length encoder, we show that good correlation coefficients with ground-truth VSSIM (ρ = 0.85) may be achieved spending, respectively, less than 12 and 27 kbps for a video sequence with CIF or SD resolution.