Abstract
Driven by the rapid development of digital imaging and network technologies, the opinion-unaware blind image quality assessment (BIQA) method has become an important yet very challenging task. In this paper, we design an effective novel scheme for opinion-unaware BIQA. We first utilize the convolutional maps to select high-contrast patches, and then we utilize these selected patches of pristine images to train a pristine multivariate Gaussian (PMVG) model. In the test stage, each high-contrast patch is fitted by a test MVG (TMVG) model, and the local quality score is obtained by comparing with the PMVG. Finally, we propose the deep activation pooling (DAP) to automatically emphasize the more important scores and suppress the less important ones so as to obtain the overall image quality score. We verify the proposed method on two widely used databases, that is, the computational and subjective image quality (CSIQ) and the laboratory for image and video engineering (LIVE) databases, and the experimental results demonstrate that the proposed method achieves better results than the state-of-the-art methods.
Highlights
Nowadays, there are many applications [1,2], such as image transmission, acquisition, compression, enhancement and analysis, that require an efficient and accurate algorithm for image quality assessment (IQA)
The first index is the Spearman rank-order correlation coefficient (SRCC), which is between the subjective mean opinion scores (MOS) and the objective
The second index is the Pearson linear correlation coefficient (PRCC), which is between the MOS and the objective IQA scores after a linear regression [23]
Summary
There are many applications [1,2], such as image transmission, acquisition, compression, enhancement and analysis, that require an efficient and accurate algorithm for image quality assessment (IQA). The IQA methods can be separated into two major classes: subjective assessment and objective assessment. As the final evaluation criterion of an image, the subjective assessment is mainly conducted by the pretrained human observers, which is time-consuming and labour-intensive. More and more researchers are devoted to the development of objective IQA methods that can automatically evaluate the image quality. One trend is to develop full reference IQA (FR–IQA). Algorithms when the pristine reference image is available. Kim et al [3] utilized the difference of gradients between the source and distorted images to evaluate the image quality
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