Quality assessment for synthesized view based on variable-length context tree

In free viewpoint television (FTV) application scenario, views that synthesized with depth image-based rendering (DIBR) techniques mainly contain special artifacts like geometric distortions. These artifacts may affect the structure of images/videos by changing the global contour characteristics and thus are annoying for human observers. Context tree based contour coding scheme can be a good tool to measure such structure loss since the more geometric distortion there is in the synthesized view the lager the gap between the encoding cost of the reference and synthesized views. In this paper, we investigate whether such overall encoding cost can be related to perceptual annoyance reflecting in quality score and propose a variable-length context tree based image quality assessment (CT-IQA) scheme. This scheme quantify (1) the overall structure dissimilarity and (2) dissimilarities in various contour characteristics between the reference and synthesized views. The proposed metric is robust to global shifting artifact that is over penalized by traditional metrics. According to the experimental results on the IRCCyn/IVC DIBR image database, the performance of the proposed CT-IQA is promising.