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Segmentation-Based and Region-Adaptive Lossless Image Compression Underpinned by a Stellar-Field Image Model

GRUNLER, Christian D (2010) Segmentation-Based and Region-Adaptive Lossless Image Compression Underpinned by a Stellar-Field Image Model. Doctoral thesis, Staffordshire University.

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Abstract or description

The central question addressed in this research is whether lossless compression of stellar-field images can be enhanced in terms of compression ratio, by using image segmentation and region-adaptive bit-allocation which are based on a suitable image model. Therefore, special properties of stellarfield images, which compression algorithms could exploit, are studied. The research proposes and develops novel lossless compression algorithms for the compaction of stellar-field images. The proposed algorithms are based on image segmentation coupled to a domain-specific image data model and to a region-adaptive allocation of pixel bits. The algorithms exploit the distinctive characteristics of stellar-field images and aim to meet the requirements for compressing scientific-quality astronomical images. The image data model used is anchored on the property of a stellar-field image encapsulated in the characterisation of this type of images as consisting of “dot-like bright objects on a noisy background”. These novel algorithms segment the dot-like bright objects, corresponding to the high-dynamic-range areas of the image, from the noise-like low-dynamic-range background sky areas. Following the segmentation of the image, the algorithms perform region-adaptive image compression tuned to each specific component of the image data model. Besides the development of novel algorithms, the research also presents a survey of the state-of-the-art of compression algorithms for astronomical images. It reviews and compares existing methods claimed to be able to achieve lossless compression of stellar-field images and contributes an evaluation of a set of existing methods. Experiments to evaluate the performance of the algorithms investigated in this research were conducted using a set of standard astronomical test images. The results of the experiments show that the novel algorithms developed in this research can achieve compression ratios comparable to, and often better than existing methods. The evaluation results show that a significant compaction can be achieved by image segmentation and region-adaptive bitallocation, anchored on a domain-specific image data model. Based on the evaluation results, this research suggests application classes for the tested algorithms. On the test image set, existing methods which do not explicitly exploit the special characteristics of astronomical images were shown to lead to average compression ratios of 1.97 up to 3.92. Great differences were found between the results on 16-bit-per-pixel images and those on 32-bit-per-pixel images. For these existing methods, the average results on 16-bit-per-pixel images range from 1.37 up to 2.81, and from 3.81 up to 6.42 for 32-bit-per-pixel images. Therefore, it is concluded that for archiving data, compression methods may indeed save costs for storage media or data transfer time, especially if a large part of the raw images is encoded with 32 bits per pixel. With average compression ratios on the test image set in the range of 3.37 to 3.82, the simplest among the new algorithms developed in this research achieved a result which is comparable to the best existing methods. These simple algorithms use general-purpose methods, which have limited performance, for encoding the data streams of separate image regions corresponding to components of a stellar-field image. The most advanced of the new algorithms, which uses data encoders tuned to each image signal component, outperformed existing methods by about 10 percent (average of 4.29 on the test image set), in terms of size efficiency; it can yield a compression ratio of 7.87. Especially for applications where high volumes of image data have to be stored, the most advanced of the new algorithms should also be considered.

Item Type: Thesis (Doctoral)
Faculty: PhD
Depositing User: Louise YARWOOD
Date Deposited: 02 Apr 2014 10:00
Last Modified: 24 Feb 2023 13:41

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