Block-based image compression with parameter-assistant inpainting

Authors:
Zhiwei Xiong;Xiaoyan Sun;Feng Wu
Affiliations:
University of Science and Technology of China, Hefei, China and Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China
Venue:
IEEE Transactions on Image Processing
Year:
2010

Citing 8
Cited 2

Second-generation image coding: an overview

ACM Computing Surveys (CSUR)
Image inpainting

Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Driving vision by topology

ISCV '95 Proceedings of the International Symposium on Computer Vision
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence
The minimum description length principle in coding and modeling

IEEE Transactions on Information Theory
Structure and texture filling-in of missing image blocks in wireless transmission and compression applications

IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting

IEEE Transactions on Image Processing
Image Compression With Edge-Based Inpainting

IEEE Transactions on Circuits and Systems for Video Technology

An efficient framework for image/video inpainting

Image Communication
Spatiotemporal texture synthesis and region-based motion compensation for video compression

Image Communication

Quantified Score

Hi-index	0.01

Visualization

Abstract

This correspondence presents an image compression approach that integrates our proposed parameter-assistant inpainting (PAI) to exploit visual redundancy in color images. In this scheme, we study different distributions of image regions and represent them with a model class. Based on that, an input image at the encoder side is divided into featured and non-featured regions at block level. The featured blocks fitting the predefined model class are coded by a few parameters, whereas the non-featured blocks are coded traditionally. At the decoder side, the featured regions are restored through PAI relying on both delivered parameters and surrounding information. Experimental results show that our method outperforms JPEG in featured regions by an average bit-rate saving of 76% at similar perceptual quality levels.