The invention relates to a non-reference three-dimensional image quality evaluation method based on a multi-dimensional attention mechanism, which comprises the following steps of: preprocessing an original three-dimensional image used for training, converting the original three-dimensional image into a gray level image, segmenting the gray level image into small non-overlapped image blocks, endowing each image block with a real quality score of the image, and obtaining a quality score of the image; randomly selecting a plurality as the input of a network model; a convolutional neural network based on multi-dimensional attention is trained, and the method comprises the following steps: (1) using a group of CCP modules of convolution and pooling operation to extract primary features from left and right views, and processing the left and right views to obtain a primary feature graph; (2) the primary feature maps of the left and right views are sent to a view fusion sub-network, and a fusion feature map is calculated; (3) inputting the fused feature map into a multi-scale feature enhancement sub-network based on multi-dimensional attention, and predicting the quality score of the image; (4) calculating a loss function of the network, and carrying out iterative training; and performing image quality evaluation by using the trained network.
Non-reference stereo image quality evaluation method based on multi-dimensional attention mechanism