Master Thesis MSTR-3359

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)


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.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Superviser(s)Wang Zhe
Entry dateDecember 5, 2012
   Publ. Computer Science