activity
20182022
most citedMuZero with Self-competition for Rate Control in VP9 Video Compression

13 citations · 15 across the 5 of their papers we have counts for

collaborators

8 papers

eess.IV202213 cited

MuZero with Self-competition for Rate Control in VP9 Video Compression

Amol Mandhane, Anton Zhernov, Maribeth Rauh +16

Video streaming usage has seen a significant rise as entertainment, education, and business increasingly rely on online video. Optimizing video compression has the potential to inc…

eess.IV2021

A Quantitative Approach To The Temporal Dependency in Video Coding

Jingning Han, Paul Wilkins, Yaowu Xu +1

Motion compensated prediction is central to the efficiency of video compression. Its predictive coding scheme propagates the quantization distortion through the prediction chain an…

cs.CV2020

Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks

Mohammad Akbari, Jie Liang, Jingning Han +1

Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rat…

cs.LG2020

Neural Rate Control for Video Encoding using Imitation Learning

Hongzi Mao, Chenjie Gu, Miaosen Wang +9

In modern video encoders, rate control is a critical component and has been heavily engineered. It decides how many bits to spend to encode each frame, in order to optimize the rat…

eess.IV2020

A Technical Overview of AV1

Jingning Han, Bohan Li, Debargha Mukherjee +12

The AV1 video compression format is developed by the Alliance for Open Media consortium. It achieves more than 30% reduction in bit-rate compared to its predecessor VP9 for the sam…

eess.IV2020

Generalized Octave Convolutions for Learned Multi-Frequency Image Compression

Mohammad Akbari, Jie Liang, Jingning Han +1

Learned image compression has recently shown the potential to outperform the standard codecs. State-of-the-art rate-distortion (R-D) performance has been achieved by context-adapti…