Light Field Image Compression Using Depth-based CNN in Intra Prediction

Abstract

Recently, light field images have received extensive attention due to their potential applications. Since they take up a huge memory because of its super-high resolution, efficient compression methods are fundamentally required. In this paper, we propose a novel intra prediction mode by using depth-adaptive convolutional neuro network (DCNN). Light field projection finds the imaging response distribution for each object point using the depth estimated from each macropixel in the light field image. The highly correlated imaging responses are used to select the neural network structure. The network structure also adapts to the to-be-encoded block size. Adding the proposed DCNN-based prediction mode into the rate-distortion optimization loop with other 35 intra prediction modes of HEVC, the proposed encoding scheme achieves a significant bit-rate saving compared to representative compression approaches with limited computational complexity increment. Statistical data are also provided and analyzed to demonstrate the efficiency of the proposed method.