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	Flexible Lossy Compression for Selective Encrypted Image With Image Inpainting 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2019, 卷号: 29, 期号: 11, 页码: 3341-3355 作者: Qin, Chuan; Zhou, Qing; Cao, Fang; Dong, Jing; Zhang, Xinpeng \| 收藏 \| 浏览/下载：84/0 \| 提交时间：2020/03/30 Image coding Encryption Image reconstruction Receivers Transforms Cloud computing Lossy compression image encryption image reconstruction image inpainting
	An improved fast parallel SPIHT algorithm and its FPGA implementation (EI CONFERENCE) 会议论文 OAI收割 2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, May 21, 2010 - May 24, 2010, Wuhan, China 作者: Jin L.-X.; Tao H.-J.; Wu Y.-H. 收藏 \| 浏览/下载：22/0 \| 提交时间：2013/03/25 In view of the current stringent need to the real-time compression algorithm of the high-speed and high-resolution image such as remote sensing or medical image and so on in this paper No List SPIHT (NLS) algorithm has been improved and a fast parallel SPIHT algorithm is proposed which is suitable to implement with FPGA. It can deal with all bit-planes simultaneously and process in the speed of 4pixels/period so the encoding time is only relative to the image resolution. The experimental results show that the processing capacity can achieve 200MPixels/s when the input clock is 50MHz the system of this paper need 2.29ms to complete lossless compression of a 512x512x8bit image and only requires 1.31ms in the optimal state. The improved algorithm keeps the high SNR unchanged increases the speed greatly and reduces the size of the needed storage space. It can implement lossless or lossy compression and the compression ratio can be controlled. It could be widely used in the field of the high-speed and high-resolution image compression. 2010 IEEE.
	The compression and storage method of the same kind of medical images-DPCM (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：16/0 \| 提交时间：2013/03/25 Medical imaging has started to take advantage of digital technology opening the way for advanced medical imaging and teleradiology. Medical images however require large amounts of memory. At over 1 million bytes per image a typical hospital needs a staggering amount of memory storage (over one trillion bytes per year) and transmitting an image over a network (even the promised superhighway) could take minutes - too slow for interactive teleradiology. This calls for image compression to reduce significantly the amount of data needed to represent an image. Several compression techniques with different compression ratio have been developed. However the lossless techniques which allow for perfect reconstruction of the original images yield modest compression ratio while the techniques that yield higher compression ratio are lossy that is the original image is reconstructed only approximately Medical imaging poses the great challenge of having compression algorithms that are lossless (for diagnostic and legal reasons) and yet have high compression ratio for reduced storage and transmission time. To meet this challenge we are developing and studying some compression schemes which are either strictly lossless or diagnostically lossless taking advantage of the peculiarities of medical images and of the medical practice. In order to increase the Signal to-Noise Ratio (SNR) by exploitation of correlations within the source signal a method of combining differential pulse code modulation (DPCM) is presented.
	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：42/0 \| 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.