Learning Structural Regularity for Evaluating Blocking Artifacts in JPEG Images

Abstract

Image degradation damages genuine visual structures and causes pseudo structures. Pseudo structures are usually present with regularities. This letter proposes a machine learning based blocking artifacts metric for JPEG images by measuring the regularities of pseudo structures. Image corner, block boundary and color change properties are used to differentiate the blocking artifacts. A support vector regression (SVR) model is adopted to learn the underlying relations between these features and perceived blocking artifacts. The blocking artifacts score of a test image is predicted using the trained model. Extensive experiments demonstrate the effectiveness of the method.