Conditional Neural Video Coding with Spatial-Temporal Super-Resolution
Henan Wang Xiaohan Pan Runsen Feng Zongyu Guo Zhibo Chen
This document is an expanded version of a one-page abstract originally
presented at the 2024 Data Compression Conference. It describes our proposed
method for the video track of the Challenge on Learned Image Compression (CLIC)
2024. Our scheme follows the typical hybrid coding framework with some novel
techniques. Firstly, we adopt Spynet network to produce accurate motion vectors
for motion estimation. Secondly, we introduce the context mining scheme with
conditional frame coding to fully exploit the spatial-temporal information. As
for the low target bitrates given by CLIC, we integrate spatial-temporal
super-resolution modules to improve rate-distortion performance. Our team name
is IMCLVC.