No-Reference Stereo Image Quality Assessment Based on Image Fusion

A no-reference stereo image quality assessment algorithm based on image fusion is proposed. The algorithm reconstructs the left and right views of the stereo image by wavelet transform and fuses them into one image. The luminance coefficient of the fused image is normalized, which keeps the brightness of each part in balance and preserves the structural information of the fused image. Finally the convolutional neural network is used to extract feature and predict regression. The experimental results show that the predicted scores of the proposed method arc in good agreement with the human subjective assessment scores.