Synergetic Assessment of Quality and Aesthetic: Approach and Comprehensive Benchmark Dataset

Quantifications of image quality and aesthetic have been regarded as two independent fields in computer vision. Generally, image quality assessment aims at measuring image distortions and image aesthetic is judged by commonly established photography rules. However, either measuring image quality or aesthetic alone is not sufficient to qualitatively rank images. Therefore, this paper puts forward the synergetic assessment of quality and aesthetic to help understand the subjective human preferences of digital pictures more comprehensively. Specifically, considering that the images of existing benchmark datasets are only labeled with single attribute, we first establish a new dataset which contains 9042 real-world images with the corresponding human rated pair-wise quality-aesthetic scores. Previously, these images are only labeled with aesthetic score, and we evaluate the subjective quality score of them, so that it can make up the lack of image dataset with double attributes. Moreover, since the existing methods are mostly designed for individual attribute prediction. We then propose a two-stream learning network to assess both quality and aesthetic of images in parallel. This network follows the top-down perception mechanism which learns from both fined grained details and holistic image layout simultaneously. Furthermore, we introduce a Channel-Diversity loss, which can be deployed in grouped convolution operation, and can constrain channels to be mutually exclusive across the spatial dimensions. To some extent, this contributes to spotlight different local discriminative regions with a finer granularity. Finally, experiments demonstrate that our method outperforms the state-of-the-art methods on our established benchmark dataset and other benchmark datasets in terms of image quality and aesthetic assessment. We hope this paper could serve as a potent reference and be useful for future research on the study of image ranking. Both the benchmark dataset and the code will be publicly available to facilitate further research.