A Hybrid Image Quality Measure for Automatic Image Quality Assessment

Abstract

Automatic image quality assessment has many diverse applications. Existing quality measures are not accurate representatives of the human perception. We present a hybrid image quality (HIQ) measure, which is a combination of four existing measures using an ‘n’ degree polynomial to accurately model the human image perception. First we undertook time consuming human experiments to subjectively evaluate a given set of training images, and resultantly formed a Human Perception Curve (HPC). Next we define a HIQ measure that closely follows the HPC using curve fitting techniques. The HIQ measure is then validated on a separate set of images by similar human subjective experiments and is compared to the HPC.The coefficients and degree of the polynomial are estimated using regression on training data obtained from human subjects. Validation of the resultant HIQ was performed on a separate validation data. Our results show that HIQ gives an RMS error of 5.1 compared to the best RMS error of 5.8 by a second degree polynomial of an individual measure HVS (Human Visual System) absolute norm (H 1) amongst the four considered metrics. Our data contains subjective quality assessment (by 100 individuals) of 174 images with various degrees of fast fading distortion. Each image was evaluated by 50 different human subjects using double stimulus quality scale, resulting in an overall 8,700 judgements.KeywordsImage QualityMean Square ErrorHuman Visual SystemImage Quality AssessmentOpinion ScoreThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.