Implementation and evaluate the no-reference image quality assessment based on spatial and spectral entropies on the different image quality databases

The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality. In this work, it suggest that a single strategy used for single database may not be sufficient; rather, The no-reference/blind image quality assessment (NR-IQA) is the most difficult due to the reference images are not available. Spatial-Spectral Entropy-based Quality (SSEQ) index has been proven successful in image modeling and feature extraction. However, it have been improve the general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features used and their relevance to perception and thoroughly evaluate the algorithm on another databases than LIVE IQA database (applied to another three databases of subjective image quality: 1. The TID database, 2. the Toyama database, and 3. the Categorical Subjective Image Quality _CSIQ_ database.). Need find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top.