Image quality prediction using synthetic and natural codebooks: comparative results
This work addresses image quality prediction for applications requiring efficient processing, but it is incremental as it builds on existing models with modifications.
The paper tackles image/video quality assessment by modifying a codebook-based model similar to CORNIA, using both natural and synthetic images for codebook construction, and shows that synthetic images can improve quality assessment results while enabling real-time CPU execution with high correlation to mean opinion scores.
We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook construction. We also demonstrate regimes of the algorithm in which real time execution on CPU is possible for sufficiently high correlations with mean opinion score (MOS). Various pooling strategies are considered as well as the problem of metric sensitivity to bitrate.