TY - GEN
T1 - COMPRESSED IMAGE QUALITY ASSESSMENT BASED ON SAAK FEATURES
AU - Zhang, Xinfeng
AU - Kwong, Sam
AU - Kuo, C.-C. Jay
PY - 2019/9
Y1 - 2019/9
N2 - Compressed image quality assessment plays an important role in image services, especially in image compression applications, which can be utilized as a guidance to optimize image processing algorithms. In this paper, we propose an objective image quality assessment algorithm to measure the quality of compressed images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art image quality assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.
AB - Compressed image quality assessment plays an important role in image services, especially in image compression applications, which can be utilized as a guidance to optimize image processing algorithms. In this paper, we propose an objective image quality assessment algorithm to measure the quality of compressed images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art image quality assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.
KW - Saak
KW - structural distortion
KW - image quality assessment
KW - compressed image
KW - HVS
UR - http://www.scopus.com/inward/record.url?scp=85076822400&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85076822400&origin=recordpage
U2 - 10.1109/ICIP.2019.8803184
DO - 10.1109/ICIP.2019.8803184
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1730
EP - 1734
BT - 2019 International Conference on Image Processing - Proceedings
PB - IEEE
T2 - 26th IEEE International Conference on Image Processing (ICIP 2019)
Y2 - 22 September 2019 through 25 September 2019
ER -