Deep learning-based switchable network for in-loop filtering in high efficiency video coding

Abstrak

The video codecs are focusing on a smart transition in this era. A future area of research that has not yet been fully investigated is the effect of deep learning on video compression. The paper’s goal is to reduce the ringing and artifacts that loop filtering causes when high-efficiency video compression is used. Even though there is a lot of research being done to lessen this effect, there are still many improvements that can be made. In This paper we have focused on an intelligent solution for improvising in-loop filtering in high efficiency video coding (HEVC) using a deep convolutional neural network (CNN). The paper proposes the design and implementation of deep CNN-based loop filtering using a series of 15 CNN networks followed by a combine and squeeze network that improves feature extraction. The resultant output is free from double enhancement and the peak signal-to-noise ratio is improved by 0.5 dB compared to existing techniques. The experiments then demonstrate that improving the coding efficiency by pipelining this network to the current network and using it for higher quantization parameters (QP) is more effective than using it separately. Coding efficiency is improved by an average of 8.3% with the switching based deep CNN in-loop filtering.