Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Heming Sun, Lu Yu, J. Katto
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引用次数: 3

Abstract

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4×4 and 8×8, fully connected networks are used, while for large blocks 16×16 and 32×32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.
基于全神经网络模式的变块大小内部预测
图像内预测是图像编码的重要组成部分。本文给出了一个完全基于神经网络模型的内部预测框架。每个NM都可以看作是从邻近参考块到当前编码块的回归。(1)对于可变块大小,我们使用不同的网络结构。对于小块4×4和8×8,使用完全连接的网络,而对于大块16×16和32×32,使用卷积神经网络。(2)对于每种预测模式,我们开发了特定的预训练网络来提高回归精度。整合到HEVC测试模型中,Y、U、V分量的bd率比锚点分别节省3.55%、3.03%、3.27%。据我们所知,这是第一次探索一个完全基于NM的帧内预测框架,与之前的工作相比,我们获得了更好的编码增益和更低的复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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