A Structural Variation Classification Model for Image Quality Assessment

Abstract

Structural information is critical for image quality assessment (IQA). In this paper, first, we propose a novel model of structural variations in images. The proposed model classifies the types of structural variation within images into four categories: slight deformations, additive impairments, detail losses, and confusing contents. This system of classification applies to most types of structural variations observed in practice. In this model, each pixel from the distorted images is classified according to its structural variation using fuzzy logic based on a set of structural features extracted from the images. Then, a novel IQA method based on these pixel classifications is proposed. This proposed method evaluates the image quality by combining two aspects: the distribution of different structural variations and the degree of structural differences.

Existing System

Visual Information Fidelity (VIF) method. Multiscale Structural Similarity (MSSIM), Structural Similarity (SSIM).

Proposed System

A novel model for structural variation classification is proposed here. In our model, structural variations are classified into four classes: slight deformations, additive impairments, detail losses, and confusing contents. We demonstrate that these four classes of structural variations are sufficiently general to describe all structural variations observed in practice. With the proposed model, we classify each pixel into a single variation class with the help of fuzzy logic. A novel IQA (Image Quality Assessment) method is proposed based on the structural variation classification.

Architecture

BLOCK DIAGRAM