A Compression Algorithm of Hyperspectral Remote Sensing Image Based on 3-D Wavelet-Fractal Coder

In this paper, the 3-D Wavelet-Fractal coder was used to compress the hyperspectral remote sensing image, which is a combination of 3-D improved set partitioning in hierarchical trees （SPIHT） coding and 3-D fractal coding. Hyperspectral image date cube was first translated by 3-D wavelet and the 3-D fractal compression ceding was applied to lowest frequency subband. The remaining coefficients of higher frequency sub-bands were encoding by 3-D improved SPIHT. We used the block set instead of the hierarchical trees to enhance SPIHT＇s flexibility. The classical eight kinds of affme transformations in 2-D fractal image compression were generalized to nineteen for the 3-D fractal image compression. The new compression method had been tested on MATLAB. The experiment results indicate that we can gain high compression ratios and the information loss is acceptable.

Source extension based on ε-entropy

It is known by entropy theory that image is a source correlated with a certain characteristic of probability. The entropy rate of the source and ε- entropy (rate-distortion function theory) are the information content to identify the characteristics of video images, and hence are essentially related with video image compression. They are fundamental theories of great significance to image compression, though impossible to be directly turned into a compression method. Based on the entropy theory and the image compression theory, by the application of the rate-distortion feature mathematical model and Lagrange multipliers to some theoretical problems in the H.264 standard, this paper presents a new the algorithm model of coding rate-distortion. This model is introduced into complete test on the capability of the test model of JM61e (JUT Test Model). The result shows that the speed of coding increases without significant reduction of the rate-distortion performance of the coder.

A reconfigurable computing architecture for 5G communication

5G baseband signal processing places greater real-time and reliability requirements on hardware.Based on the architecture of the MaPU,a reconfigurable computing architecture is proposed according to the characteristics of the 5G baseband signal processing.A dedicated instruction set for 5G baseband signal processing is proposed.The corresponding functional units are designed for reuse of hardware resources.A redirected register file is proposed to address latency and power consumption issues in internetwork.A two-dimensional code compression scheme is proposed for cases in which the use ratio of instruction memory is low.The access mode of the data memory is extended,the performance is improved and the power consumption is reduced.The throughput of 5G baseband processing algorithm is one to two orders of magnitude higher than that of the TMS320C6670 with less power consumption.The silicon area evaluated by layout is 5.8 mm2,which is 1/6 of the MaPU’s.The average power consumption is 0.7 W,which is 1/5 of the MaPU’s.

An Efficient Compression Coding System for Motion Images

An efficient compression coding system for motion images is presented. A macro-block matchingtechnique based on tile correlation betweell motion vectors is applied to the system. This technique effectivelyimproves the accuracy and the speed of motion estimation, and the compression ratio. A compression ratio of 29 isattainable without visible image degradation.

Feature-based Image Sequence Compression Coding

A novel compressing method for video teleconference applications is presented. Semantic-based coding based on human image feature is realized, where human features are adopted as parameters. Model-based coding and the concept of vector coding are combined with the work on image feature extraction to obtain the result.

Irregular Segmented Region Compression Coding Based on Pulse Coupled Neural Network

An irregular segmented region coding algorithm based on pulse coupled neural network(PCNN) is presented. PCNN has the property of pulse-coupled and changeable threshold, through which these adjacent pixels with approximate gray values can be activated simultaneously. One can draw a conclusion that PCNN has the advantage of realizing the regional segmentation, and the details of original image can be achieved by the parameter adjustment of segmented images, and at the same time, the trivial segmented regions can be avoided. For the better approximation of irregular segmented regions, the Gram-Schmidt method, by which a group of orthonormal basis functions is constructed from a group of linear independent initial base functions, is adopted. Because of the orthonormal reconstructing method, the quality of reconstructed image can be greatly improved and the progressive image transmission will also be possible.

Construction of deterministic sensing matrix and its application to DOA estimation

Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.

The Generation of a Sort of Fractal Graphs

We present an approach for generating a sort of fractal graphs by a simpleprobabilistic logic neuron network and show that the graphs can be representedby a set of compressed codings. An algorithm for quickly finding the codings,i.e., recognizing the corresponding graphs, is given. The codings are shown tobe optimal. The results above possibly give us the clue for studying imagecompression and pattern recognition.