Quality Assessment of DIBR-Synthesized Image Based on Multi-Scale Feature Fusion Deep Neural Network

With the improvement of Depth Image-Based Rendering (DIBR) technology, some more complex distortions (e.g., stretching distortion) have appeared in DIBR-synthesized images, and the DIBR Image Quality Assessment (IQA) algorithm, which was previously designed for some specific distortions (e.g., black holes, blurring), have been ineffective. In this paper, an effective metric was proposed for the new DIBR-synthesized image dataset (in which the most current distortion types are contained), to simulate the multi-scale visual properties of the human eye, we use Gaussian pyramid to obtain a multiscale representation of an image, then feature fusion of the reference and the synthesized image using the pre-trained convolutional neural network Densenet201, finally use a cosine similarity to measure the difference between the feature maps as the deviation between the reference and synthesized image. Finally, the experimental result of this algorithm on the latest DIBR-synthesized image dataset show that the model proposed in this paper is highly competitive with other IQA methods.