A novel metric for full-reference image quality assessment (IQA) is proposed in this paper. Based on the sparse representation in independent component analysis (ICA) domain, the image basis is generated from natural images adaptively, which coincides with the characteristics of human vision system (HVS). In order to extract the feature vector, a hybrid norm optimization strategy is introduced for achieving more stable computational performances. The proposed IQA metric is calculated as a correlation coefficient between the two feature vectors from reference and distorted images, respectively. Experimental results on the LIVE Database Release 2 demonstrate that the proposed metric can achieve competitive performances as compared to the well-known structural similarity (SSIM) metric.
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机译：本文提出了一种用于全参考图像质量评估（IQA）的新颖度量。基于独立分量分析（ICA）域中的稀疏表示，自适应地从自然图像生成图像基础，这与人类视觉系统（HVS）的特征相吻合。为了提取特征向量，引入了混合范数优化策略以实现更稳定的计算性能。分别从参考图像和失真图像中，将建议的IQA度量计算为两个特征向量之间的相关系数。 LIVE Database Release 2上的实验结果表明，与众所周知的结构相似性（SSIM）度量标准相比，该提议的度量标准可以实现竞争性能。
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