BLIND IMAGE QUALITY ASSESSMENT WITH LOCAL CONTRAST FEATURES

The aim of this research is to create a tool to evaluate distortion in images without the information about original image. Work is to extract the statistical information of the edges and boundaries in the image and to study the correlation between the extracted features. Change in the structural information like shape and amount of edges of the image derives quality prediction of the image. Local contrast features are effectively detected from the responses of Gradient Magnitude (G) and Laplacian of Gaussian (L) operations. Using the joint adaptive normalisation, G and L are normalised. Normalised values are quantized into M and N levels respectively. For these quantised M levels of G and N levels of L, Probability (P) and conditional probability(C) are calculated. Four sets of values namely marginal distributions of gradient magnitude Pg, marginal distributions of Laplacian of Gaussian Pl, conditional probability of gradient magnitude Cg and probability of Laplacian of Gaussian Cl are formed. These four segments or models are Pg, Pl, Cg and Cl. The assumption is that the dependencies between features of gradient magnitude and Laplacian of Gaussian can formulate the level of distortion in the image. To find out them, Spearman and Pearson correlations between Pg, Pl and Cg, Cl are calculated. Four different correlation values of each image are the area of interest. Results are also compared with classical tool Structural Similarity Index Measure (SSIM)