AN INFORMATION-THEORETIC IMAGE QUALITY MEASURE: COMPARISON WITH STATISTICAL SIMILARITY

An Information-Theoretic Image Quality Measure: Comparison with Statistical Similarity

Asmhan Flieh Hassan, Dong Cai-lin and Zahir M. Hussain PhD Candidate, School of Mathematics and Statistics, HuaZhong Normal University, 152 Luoyu Road, Wuhan, 430079, China Department of Mathematics University of Kufa, Najaf, Iraq School of Mathematics and Statistics, HuaZhong Normal University, 152 Luoyu Road, Wuhan, 430079, China Department of Computer Science, University of Kufa, Najaf, Iraq...

An Information-Theoretic Measure for Face Recognition: Comparison with Structural Similarity

Automatic recognition of people faces is a challenging problem that has received significant attention from signal processing researchers in recent years. This is due to its several applications in different fields, including security and forensic analysis. Despite this attention, face recognition is still one among the most challenging problems. Up to this moment, there is no technique that pr...

Quantization with an Information-Theoretic Distortion Measure

New Structural Similarity Measure for Image Comparison

Subjective quality measures based on Human Visual System for images do not agree well with well-known metrics such as Mean Squared Error and Peak Signal to Noise Ratio. Recently, Structural Similarity Measure (SSIM) has received acclaim due to its ability to produce results on a par with Human Visual System. However, experimental results indicate that noise and blur seriously degrade the perfor...

An Information-Theoretic Sentence Similarity Metric

We describe an information theoretic-based metric for sentence similarity. The method uses the information content (IC) of dependency triples using corpus statistics generated by processing the Open American National Corpus (OANC) with the Stanford Parser. We define the similarity of two sentences as a function of (1) the similarity of their constituent dependency triples, and (2) the position ...