Perceptual video quality assessment for adaptive streaming encoding

Adaptive video streaming has become prominent due to the rising diversity of Web-enabled personal devices. Common limitations in bandwidth and decoding power challenge the efficiency of content encoders to preserve visual quality at reduced data rates over a wide range of display resolutions. Objective assessment of perceptual video quality has greatly improved in the past decade but remains an open problem. Among the most relevant metrics are the many variations of the Structural Similarity (SSIM) index. In this work, several SSIM-based metrics are compared, optimized and improved towards better correlation with human perception by testing the HD content of the LIVE Mobile Video Quality Database. A shifted gradient is proposed to preserve more image feature information for similarity comparison, thus increasing accuracy, along with a down-sampling box pooling filter that coherently emulates Gaussian pooling while reducing computation complexity by a factor of four and providing broader scalability.