Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

Abstract

SUMMARYImage quality assessment (IQA) plays an important role in quality monitoring, evaluation and optimization for image processing systems. However, current quality-aware feature extraction methods for IQA can hardly balance accuracy and complexity. This paper introduces multi-order local description into image quality assessment for feature extraction. The first-order structure derivative and high-order discriminative information are integrated into local pattern representation to serve as the quality-aware features. Then joint distributions of the local pattern representation are modeled by spatially enhanced histogram. Finally, the image quality degradation is estimated by quantifying the divergence between such distributions of the reference image and those of the distorted image. Experimental results demonstrate that the proposed method outperforms other state-of-the-art approaches in consideration of not only accuracy that is consistent with human subjective evaluation, but also robustness and stability across different distortion types and various public databases. It provides a promising choice for image quality assessment development. key words: image quality assessment, image quality degradation, local pattern representation, feature extraction, visual perception

Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features

“…To overcome these two drawbacks, Zhang et al [41], presented the local derivative pattern (LDP), which can describe the local structural primitives of an image by extracting more detailed texture features from high-order derivatives in four directions. In [44,45], the LDP is adopted to construct the FR IQA model. Inspired by the above literature, in the proposed NR BSRSF method, the LDP is introduced to extract the discriminative texture features of pictorial regions in the spatial domain.…”