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	Orthogonal sparse fractal coding algorithm based on image texture feature 期刊论文 OAI收割 IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 11, 页码: 1872-1879 作者: Cao, Jian; Zhang, Aihua; Shi, Lei \| 收藏 \| 浏览/下载：216/0 \| 提交时间：2019/10/11 image coding feature extraction fractals data compression image texture image reconstruction orthogonal codes image colour analysis transforms decomposition matrix algebra orthogonal sparse grey level sparse decomposition decoding speed orthogonal sparse fractal coding algorithm image reconstruction quality image texture feature extraction fractal image compression coding algorithm grey description feature extraction orthogonal sparse grey level transform
	Underwater Acoustic Image Encoding Based on Interest Region and Correlation Coefficient 期刊论文 OAI收割 COMPLEXITY, 2018, 页码: 13 作者: Liu Lixin; Wu Jinqiu; Guo Feng \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2018/12/05
	Fast sparse fractal image compression 期刊论文 OAI收割 PLOS ONE, 2017, 卷号: 12, 期号: 9, 页码: 18 作者: Wang, Jianji; Chen, Pei; Xi, Bao; Liu, Jianyi; Zhang, Yi \| 收藏 \| 浏览/下载：19/0 \| 提交时间：2019/12/16
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.
	Wavelet-fractal based compression of ophthalmic image (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Xiu-Ying Z.; Lin-Pei Z. 收藏 \| 浏览/下载：13/0 \| 提交时间：2013/03/25 This study is designed to determine the degree and methods of digital image compression to produce ophthalmic images of sufficient quality for transmission and diagnosis. Fractal based compression techniques which provide a large compression ratio for grayscale images have been reported in the literature. Fractal coding is based on the fractal theory of iterated transformations. But it's searching and mapping algorithm cannot address the need of real-time. To improve the real-time performance of the algorithm we use wavelet transforms to decompose images. The wavelet transform is a natural tool for analyzing fractal block coders since wavelet bases possess the same type of dyadic self-similarity that fractal coders seek to exploit. We propose a method of fractal coding the weighted wavelet subtree. Experimented results show that the improved hybrid image can improve the PSNR of the rebuild image at the same compression ratio.