关键词: 无参考图像质量评价, 稀疏表示, 统计特征, 小波交换

Abstract: Existing blind image quality assessment (BIQA) algorithms usually build a mapping model by support vector regression (SVR) or neural network. These algorithms need a large number of samples; they are weak in generalization (perform well when training and testing on the same database, but poorly on different one). In order to overcome these deficiencies, this paper proposes a BIQA algorithm based on sparse representation. It utilizes the joint statistics information of gradient magnitude (GM) map and Laplacian of Gaussian (LOG) response and the correlation in the wavelet subbands to compose a feature dictionary, then represents the feature of a test image via sparse coding, finally estimates the differential mean opinion scores (DMOS) of dictionary images and sparse coefficient to get the predicted quality scores. The experimental results on multiple databases show that, compared with other algorithms, the proposed algorithm performs more accurately and stably under low ratio of training samples, and possesses better generalization ability and stability.

Key words:  blind image quality assessment, sparse representation, statistic feature, wavelet transform