Lossless Compression for Space Imagery in a Dynamically Reconfigurable Architecture

Authors:
Xiaolin Chen;C. Nishan Canagarajah;Raffaele Vitulli;Jose L. Nunez-Yanez
Affiliations:
Department of Electrical and Electronic Engineering, University of Bristol, UK;Department of Electrical and Electronic Engineering, University of Bristol, UK;On-Board Payload Data Processing Section, European Space Agency (ESA), The Netherlands;Department of Electrical and Electronic Engineering, University of Bristol, UK
Venue:
ARC '08 Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications
Year:
2008

Citing 5
Cited 1

JPEG 2000: Image Compression Fundamentals, Standards and Practice

JPEG 2000: Image Compression Fundamentals, Standards and Practice
NASA Advances Robotic Space Exploration

Computer
LOCO-I: a low complexity, context-based, lossless image compression algorithm

DCC '96 Proceedings of the Conference on Data Compression
A Configurable Statistical Lossless Compression Core Based on Variable Order Markov Modeling and Arithmetic Coding

IEEE Transactions on Computers
A new, fast, and efficient image codec based on set partitioning in hierarchical trees

IEEE Transactions on Circuits and Systems for Video Technology

Lossless and Near-Lossless Image Compression Scheme Utilizing Blending-Prediction-Based Approach

ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel dynamically reconfigurable hardware architecture for lossless compression and its optimization for space imagery. The proposed system makes use of reconfiguration to support optimal modeling strategies adaptively for data with different dimensions. The advantage of the proposed system is the efficient combination of different compression functions. For image data, we propose a new multi-mode image model which can detect the local features of the image and use different modes to encode regions with different features. Experimental results show that our system improves compression ratios of space image while maintaining low complexity and high throughput.