  Traditional intra prediction methods for HEVC rely on using the nearest
reference lines for predicting a block, which ignore much richer context
between the current block and its neighboring blocks and therefore cause
inaccurate prediction especially when weak spatial correlation exists between
the current block and the reference lines. To overcome this problem, in this
paper, an intra prediction convolutional neural network (IPCNN) is proposed for
intra prediction, which exploits the rich context of the current block and
therefore is capable of improving the accuracy of predicting the current block.
Meanwhile, the predictions of the three nearest blocks can also be refined. To
the best of our knowledge, this is the first paper that directly applies CNNs
to intra prediction for HEVC. Experimental results validate the effectiveness
of applying CNNs to intra prediction and achieved significant performance
improvement compared to traditional intra prediction methods.
