Intra prediction is a vital part of the image/video coding framework, which is designed to remove spatial redundancy within a picture. Based on a set of predefined linear combinations, traditional intra prediction cannot cope with coding blocks with irregular textures. To tackle this drawback, in this article, we propose a Generative Adversarial Network (GAN)-based intra prediction approach to enhance intra prediction accuracy. Specifically, with the superior non-linear fitting ability, the well-trained generator of GAN acts as a mapping from the adjacent reconstructed signals to the prediction unit, implemented into both encoder and decoder. Simulation results show that for All-Intra configuration, our proposed algorithm achieves, on average, a 1.6% BD-rate cutback for luminance components compared with video coding reference software HM-16.15 and outperforms previous similar works.
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