Characterizing Generalized Rate-Distortion Performance of Video Coding : An Eigen Analysis Approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

View graph of relations

Author(s)

  • Zhengfang Duanmu
  • Wentao Liu
  • Zhuoran Li
  • Kede Ma
  • Zhou Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9079615
Pages (from-to)6180-6193
Journal / PublicationIEEE Transactions on Image Processing
Volume29
Online published27 Apr 2020
Publication statusPublished - 2020

Abstract

Rate-distortion (RD) theory is at the heart of lossy data compression. Here we aim to model the generalized RD (GRD) trade-off between the visual quality of a compressed video and its encoding profiles (e.g., bitrate and spatial resolution). We first define the theoretical functional space \mathcal {W} of the GRD function by analyzing its mathematical properties. We show that \mathcal {W} is a convex set in a Hilbert space, inspiring a computational model of the GRD function, and a method of estimating model parameters from sparse measurements. To demonstrate the feasibility of our idea, we collect a large-scale database of real-world GRD functions, which turn out to live in a low-dimensional subspace of \mathcal {W}. Combining the GRD reconstruction framework and the learned low-dimensional space, we create a low-parameter eigen GRD method to accurately estimate the GRD function of a source video content from only a few queries. Experimental results on the database show that the learned GRD method significantly outperforms state-of-the-art empirical RD estimation methods both in accuracy and efficiency. Last, we demonstrate the promise of the proposed model in video codec comparison.

Research Area(s)

  • Quadratic programming, Rate-distortion function, Video quality assessment