Video compression with low complexity CNN-based spatial resolution adaptation

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

It has recently been demonstrated that spatial resolution adaptation can be integrated within video compression to improve overall coding performance by spatially down-sampling before encoding and superresolving at the decoder. Significant improvements have been reported when convolutional neural networks (CNNs) were used to perform the resolution upsampling. However, this approach suffers from high complexity at the decoder due to the employment of CNN-based super-resolution. In this paper, a novel framework is proposed which supports the flexible allocation of complexity between the encoder and decoder. This approach employs a CNN model for video downsampling at the encoder and uses a Lanczos3 filter to reconstruct full resolution at the decoder. The proposed method was integrated into the HEVC HM 16.20 software and evaluated on JVET UHD test sequences using the All Intra configuration. The experimental results demonstrate the potential of the proposed approach, with significant bitrate savings (more than 10%) over the original HEVC HM, coupled with reduced computational complexity at both encoder (29%) and decoder (10%).
Original languageEnglish
Title of host publicationVideo compression with low complexity CNN-based spatial resolution adaptation
Publication statusAccepted/In press - 20 Apr 2020
EventApplications of Digital Image Processing XLIII -
Duration: 24 Aug 202028 Aug 2020

Conference

ConferenceApplications of Digital Image Processing XLIII
Period24/08/2028/08/20

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