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	CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 7, 页码: 4138-4149 作者: Zhao, Hengrun; Zheng, Bolun; Yuan, Shanxin; Zhang, Hua; Yan, Chenggang \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2022/12/07 Image coding Quantization (signal) Streaming media Bit rate Image restoration Transform coding Video recording Quality enhancement CBR compressed video dual-domain restoration
	Compressed Coding, AMP-Based Decoding, and Analog Spatial Coupling 期刊论文 OAI收割 IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 卷号: 68, 期号: 12, 页码: 7362-7375 作者: Liang, Shansuo; Liang, Chulong; Ma, Junjie; Ping, Li \| 收藏 \| 浏览/下载：128/0 \| 提交时间：2021/04/26 Decoding Error correction Encoding Error correction codes Signal to noise ratio Modulation Compressed sensing Compressed sensing forward error control coding approximate message passing state evolution area theorem and analog spatial-coupling
	Study on Single Dispersion Spectral Imager Based on Compressed Coding 期刊论文 OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 卷号: 37, 期号: 9, 页码: 2919-2926 作者: Tang Xing-jia; Li Li-bo; Zhao Qiang; Li Hong-bo; Hu Bing-liang \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2017/12/30 Compressed Coding Spectral Imaging Multi-frame Reconstruction Single Dispersion
	Image coding using wavelet-based compressive sampling (EI CONFERENCE) 会议论文 OAI收割 2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China 作者: Li J.; Li J.; Li J. 收藏 \| 浏览/下载：49/0 \| 提交时间：2013/03/25 In this paper we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. After multi-scale DWT the low-frequency sub-band and high-frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients we allocate different measurements to low- and high-frequency component. Then the measurements samples can be quantized. The quantize samples are entropy coded and forward correct coding (FEC). Finally the compressed streams are transmitted. At the decoder one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.
	Compression of remote sensing image based on Listless Zerotree Coding and DPCM (EI CONFERENCE) 会议论文 OAI收割 ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China Chen S.-L.; Huang L.-Q. 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/03/25 The data quantity of remote sensing image is very large. Furthermore the lowest frequency subband contains the main energy of original image and reflects the coarse of original image after remote sensing image is transformed by wavelet so it is very important to the reconstructed image. Therefore a hybrid image compression method based on Listless Zerotree Coding (LZC) and DPCM is presented namely the lowest frequency subband is compressed by DPCM and others are compressed by LZC. LZC is a kind of zerotree coding algorithm for hardware implementation which is based on SPIHT and substitutes two significant bit maps for three lists in SPIHT algorithm. Thereby LZC significantly reduces the memory requirement and complexity during encoding and decoding procedure. But LZC doesn't recognize the significance of grandchild sets so the PSNR values of LZC are lower than SPIHT's and the compression speed drops. It is improved by adding a significant bit map that recognizes the significance of grandchild sets. A comparison reveals that the PSNR results of the hybrid compression method are 2 dB higher than those of LZC and the compression speed is also improved.
	Study on CCD image compression and mass storage (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：36/0 \| 提交时间：2013/03/25 When CCD camera photographs massive images in the air it is very important to compress and save CCD image timely and validly. This paper designs the CCD image compression and mass storage system. The one part is image compression: this paper introduces a reduced memory still image compression algorithm based on Listless Zerotree Coding (LZC). Compared with SPIHT the approach significantly reduced memory requirement and no reducing the quality of the reconstructed image. The other part is image mass storage: this system uses a kind of special hard disk storage devices that can realize faster data transmission to SCSI (Small Computer System Interface) devices even if it separates oneself from PCs. In the faster data acquisition and storage system data storage is a key technology. Normal approach is saving the data to mass memories and then processing and saving the data after complete acquisition. The continuous acquisition time is restricted with the storage capacity in the normal method so that it can't receive the requirement of CCD image storage on many occasions. While its price will be geminate increasing when we increase the storage capacity. So the approach in this paper is better one to use fast disks on data direct mass storage considering the storage capacity read/write speed and unit cost. The result of the experiment shows that the system has compressed and saved CCD image validly so it reached the anticipative purpose.
	Wavelet packet and neural network basis medical image compression (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 Zhao X.; Wei J.; Zhai L. 收藏 \| 浏览/下载：33/0 \| 提交时间：2013/03/25 It is difficult to get high compression ratio and good reconstructed image by conventional methods we give a new method of compression on medical image. It is to decompose and reconstruct the medical image by wavelet packet. Before the construction the image use neural network in place of other coding method to code the coefficients in the wavelet packet domain. By using the Kohonen's neural network algorithm not only for its vector quantization feature but also for its topological property. This property allows an increase of about 80% for the compression rate. Compared to the JPEG standard this compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30. This method can get big compression ratio and perfect PSNR. Results show that the image can be compressed greatly and the original image can be recovered well. In addition the approach can be realized easily by hardware.