New Adaptive Image Quality Assessment Based on Distortion Classification

• Main Authors: Xin JIN, Mei YU, Gangyi JIANG, Feng SHAO, Fen CHEN, Zongju PENG
• Format: Article
• Language: English
• Published: IFSA Publishing, S.L. 2014-01-01
• Series: Sensors & Transducers
• Subjects: Image quality assessment; Structural similarity; Wavelet based structural similarity; Distortion analysis; Adaptive metric selection.
• Online Access: http://www.sensorsportal.com/HTML/DIGEST/january_2014/Vol_163/P_1802.pdf

This paper proposes a new adaptive image quality assessment (AIQA) method, which is based on distortion classifying. AIQA contains two parts, distortion classification and image quality assessment. Firstly, we analysis characteristics of the original and distorted images, including the distribution of wavelet coefficient, the ratio of edge energy and inner energy of the differential image block, we divide distorted images into White Noise distortion, JPEG compression distortion and fuzzy distortion. To evaluate the quality of first two type distortion images, we use pixel based structure similarity metric and DCT based structural similarity metric respectively. For those blurriness pictures, we present a new wavelet-based structure similarity algorithm. According to the experimental results, AIQA takes the advantages of different structural similarity metrics, and it’s able to simulate the human visual perception effectively.