中国科学院机构知识库网格系统: 检索

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

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收藏 | 浏览/下载：35/0 | 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

How much information is needed in quantized nonlinear control? 期刊论文 OAI收割
SCIENCE CHINA-INFORMATION SCIENCES, 2018, 卷号: 61, 期号: 9, 页码: 13
作者: Zheng, Chuang; Li, Lin; Wang, Leyi; Li, Chanying

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收藏 | 浏览/下载：37/0 | 提交时间：2018/07/30

nonlinear systems disturbances input-to-state stabilizability sampled systems quantization rate

Quantized Leaderless and Leader-Following Consensus of High-Order Multi-Agent Systems With Limited Data Rate 期刊论文 OAI收割
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2016, 卷号: 61, 期号: 9, 页码: 2432-2447
作者: Qiu, Zhirong; Xie, Lihua; Hong, Yiguang

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收藏 | 浏览/下载：36/0 | 提交时间：2018/07/30

Data rate distributed consensus high-order dynamics integrator systems multi-agent systems quantization

Distributed consensus over digital networks with limited bandwidth and time-varying topologies 期刊论文 OAI收割
AUTOMATICA, 2011, 卷号: 47, 期号: 9, 页码: 2006-2015
作者: Li, Tao; Xie, Lihua

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收藏 | 浏览/下载：18/0 | 提交时间：2018/07/30

Multi-agent systems Distributed consensus Data rate Quantization Time-varying topology

Distributed Consensus With Limited Communication Data Rate 期刊论文 OAI收割
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2011, 卷号: 56, 期号: 2, 页码: 279-292
作者: Li, Tao; Fu, Minyue; Xie, Lihua; Zhang, Ji-Feng

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收藏 | 浏览/下载：21/0 | 提交时间：2018/07/30

Average-consensus communication energy data rate distributed consensus distributed coordination distributed estimation multi-agent systems quantization sensor network

Space camera imaging gain in-orbit adjusting strategy (EI CONFERENCE) 会议论文 OAI收割
2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, October 10, 2009 - October 11, 2009, Changsha, Hunan, China
作者: Wang J.; He X.

收藏 | 浏览/下载：16/0 | 提交时间：2013/03/25

A Multi-Step Gain Adjusting Strategy (MSGAS) of space camera was proposed which was used to get higher SNR (Signal-Noise Rate) image. When the space camera working in poor light condition the CCD signal was so weak that it's difficult to get a clear image to reduce the quantization noise and improve the SNR we must amplify the CCD signal first and then quantize. The MSGAS was achieved by adjusting the gain of the CCD (Charge Couple Device) signal processor step by step the upper limit and lower limit were set if the MDN (Mean Digital Number) of a fixed length image data was not between the lower and upper limit the gain was adjusted step by step. In the experiment the upper limit lower limit and the step were set and the result of the experiment showed that MSGAS was robust and SNR was improved from 28 to 39. 2009 IEEE.

Statistical model, analysis and approximation of rate-distortion function in mpeg-4 fgs videos 期刊论文 iSwitch采集
Ieee transactions on circuits and systems for video technology, 2006, 卷号: 16, 期号: 4, 页码: 535-539
作者: Sun, J; Gao, W; Zhao, DB; Huang, QM

收藏 | 浏览/下载：26/0 | 提交时间：2019/05/10

Fine granularity scalability (fgs) Mpeg-4 Quantization theory Rate-distortion (r-d) Statistical model

Statistical model, analysis and approximation of rate-distortion function in MPEG-4 FGS videos 期刊论文 OAI收割
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2006, 卷号: 16, 期号: 4, 页码: 535-539
作者: Sun, J; Gao, W; Zhao, DB; Huang, QM

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收藏 | 浏览/下载：24/0 | 提交时间：2019/12/16

fine granularity scalability (FGS) MPEG-4 quantization theory rate-distortion (R-D) statistical model

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：37/0 | 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 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：20/0 | 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.