Master Thesis MSTR-3359

BibliographyHijazi, Haytham W.: LOCMIC:LOW COMPLEXITY MULTI-RESOLUTION IMAGE COMPRESSION.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 3359 (2012).
73 pages, english.
CR-SchemaE.4 (Data Coding and Information Theory)
I.4.1 (Digitization and Image Capture)
I.4.2 (Image Processing and Computer Vision Compression (Coding))
I.4.10 (Image Representation)
I.6 (Simulation and Modeling)
Abstract

ABSTRACT

Image compression is a well-established and extensively researched field. The huge interest in it has been aroused by the rapid enhancements introduced in imaging techniques and the various applications that use high-resolution images (e.g. medical, astronomical, Internet applications). The image compression algorithms should not only give state-of-art performance, they should also provide other features and functionalities such as progressive transmission. Often, a rough approximation (thumbnail) of an image is sufficient for the user to decide whether to continue the image transmission or to abort; which accordingly helps to reduce time and bandwidth. That in turn necessitated the development of multi-resolution image compression schemes. The existed multi-resolution schemes (e.g., Multi-Level Progressive method) have shown high computational efficiency, but with a lack of the compression performance, in general. In this thesis, a LOw Complexity Multi-resolution Image Compression (LOCMIC) based on the Hierarchical INTerpolation (HINT) framework is presented. Moreover, a novel integration of the Just Noticeable Distortion (JND) for perceptual coding with the HINT framework to achieve a visual-lossless multi-resolution scheme has been proposed. In addition, various prediction formulas, a context-based prediction correction model and a multi-level Golomb parameter adaption approach have been investigated.

The proposed LOCMIC (the lossless and the visual lossless) has contributed to the compression performance. The lossless LOCMIC has achieved a 3% reduced bit rate over LOCO-I, about 1% over JPEG2000, 3% over SPIHT, and 2% over CALIC. The Perceptual LOCMIC has been better in terms of bit rate than near-lossless JPEG-LS (at NEAR=2) with about 4.7%. Moreover, the decorrelation efficiency of the LOCMIC in terms of entropy has shown an advance of 2.8%, 4.5% than the MED and the conventional HINT respectively.

Full text and
other links
PDF (2140592 Bytes)
Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Superviser(s)Wang Zhe
Entry dateDecember 5, 2012
   Publ. Computer Science