关键词: 图像质量评估, 权重池, 感受野, 多尺度评估

Abstract:

Image quality assessment often takes human subjective evaluation as the final measurement standard. However, manual assessment is time-consuming and cumbersome, and it cannot be applied to real-time images or video sequences. In the quality assessment system, therefore, a predictive image quality algorithm that aims to imitate human subjectivity has important value. In response to the above problems, this paper designs a convolutional neural network for local image quality assessment, which forms a more effective image quality assessment model by integrating feature learning and regression into an optimization process. According to human visual habits, this paper uses the eye tracker’s viewpoint distribution map to generate a weight pool based on visual importance. Experiments show that the design of the weight pool can achieve the best overall performance. The original image is low-pass filtered and down-sampled. The weight coefficient decay strategy is used in the down-sampling process, and the multi-scale image is used for weighted quality comprehensive assessment. The results prove that the multi-scale assessment method effectively improves the assessment model. The method in this paper can achieve excellent performance on the LIVE database and has good generalization ability.

Key words: image quality assessment, weight pool, receptive field, multi-scale assessment