<title>Block Character Coding</title>

The two-dimensional rectangular cutting problem with defects is discussed. The goal is to cut a small rectangular block of a given height and width from a large rectangular object containing a plurality of defects on the premise of satisfying several constraints, so that the sum of the area of the cut small rectangular blocks are maximized. The constraint is that each cutting operation must be guillotine, and the number of small rectangular blocks of each type is not limited and maintains a given direction. The problem is regarded as covering the original plate with small rectangular block, and a Quick Heuristic-Dynamic Programming (QHDP) algorithm is proposed. Firstly, a one-dimensional knapsack problem is established according to the height and width of small rectangular blocks, respectively, and an efficient discretization sets is generated respectively. Then, each value in the sets is used as a possible cutting line coordinate for sub-problem division. The algorithm calculates 14 typical examples. The experimental results show that it has obtained the optimal solution of all the examples, and the calculation time is less than one tenth of the best algorithm in the current literature. The algorithm complexity is analyzed and proved.

In this paper, we describe a compression scheme for encoding time-varying isosurfaces and amorphous volumetric features (volumes within specified value ranges) in a unified way, which allows for on-line reconstruction and rendering. Since the size of even one frame in a time-varying data set is very large, transmission and on-line reconstruction are the main bottlenecks for interactive visualization of time-varying volume and surface data. To increase the run-time decompression speed and compression ratio, we decompose the volume into small blocks and encode only the significant blocks that contribute to the isosurface and volumetric features. The result shows that our compression scheme achieves high compression ratio with fast reconstruction, which is effective for client-side rendering of time-varying isosurfaces with amorphous volumetric features.

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time. Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle to involve four types of binary code books (i.e. Pour when , Flat when , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding procedure, with very small distortion per block, by designing small binary codebook, then rotating each block in it. Moreover, it can be used for improving the efficiency of the coding process even further with the decrease in the bit rate (i.e. increasing the compression ratio(.

This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical- next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and open MP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.

String dictionaries constitute a large portion of the memory footprint of database applications. While strong string dictionary compression algorithms exist, these come with impractical access and compression times. Therefore, lightweight algorithms such as front coding (PFC) are favored in practice. This paper endeavors to make strong string dictionary compression practical. We focus on Re-Pair Front Coding (RPFC), a grammar-based compression algorithm, since it consistently offers better compression ratios than other algorithms in the literature. To accelerate compression times, we propose block-based RPFC (BRPFC) which consists in independently compressing small blocks of the dictionary. For further accelerated compression times especially on large string dictionaries, we also propose an alternative version of BRPFC that uses sampling to speed up compression. Moreover, to accelerate access times, we devise a vectorized access method, using hbox {Intel}^{circledR } Advanced Vector Extensions 512 (hbox {Intel}^{circledR } AVX-512). Our experimental evaluation shows that sampled BRPFC offers compression times up to 190 times faster than RPFC, and random string lookups 2.3 times faster than RPFC on average. These results move our modified RPFC into a practical range for use in database systems because the overhead of Re-Pair-based compression for access times can be reduced by 2 times .

Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.

In common wireless scenario, the data needs to be efficiently compressed by utilizing the channel bandwidth and thus to increase the data bit rate. The image is transmitted over the channel block by block. We can enhance the compression performance by intelligently choosing blocks from the image with non-relevant information in it. By removing these small blocks, we can reconstruct the lost data using correlation between the lost blocks and its neighbors. This is automatically done in a fast way by using the boundary information, thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. This paper addresses the issue of performing inpainting on the decompressed image to fill in the missing information. The effectiveness of this approach is demonstrated with various compression factors. Quantitative analysis of the desired compression ratio and the quality of the restored image is conducted.

The article is devoted to methods for constructing GL-models of fault-tolerant multiprocessor systems. GL-models can be used as behavioral models of such systems under failure flows to evaluate their reliability metrics through statistical experiments. The study considers two types of systems: consecutive two-dimensional systems and mixed-type systems. A consecutive two-dimensional system is defined as one in which components are arranged in the form of a rectangular matrix, and the system fails when a rectangular block of a certain size appears, consistingentirely of failed components. A mixed-type system fails if at least one of the following conditions is met: a specified number of arbitrary components have failed; a specified number of consecutive components have failed; or a rectangular block of a certain size, consisting entirely of failed components, appears within the component matrix. Currently, there are no formalized methods for constructing GL-models for the aforementioned types of systems. The objective of this work is to develop a universal method for constructing GL-models for both consecutive two-dimensional systems and mixed-type systems. It is shown that, to construct a GL-model for such a system, it is sufficient to determine the maximum number of failed components under which the system remains operational. Based on this threshold, a basic system model is constructed without considering additional failure conditions. Then, all combinations of component failures that lead to system failure are identified. The basic model is subsequently weakened at the vectors corresponding to these critical failure combinations. This paper presents, for the first time, an algorithm for constructing GL-models for consecutive two-dimensional systems and mixed-type systems. In addition, it introduces methods for calculating the maximum allowable number of component failures under which the system remains functional, as well as estimating the total number of failure combinations that result in system failure. Experimental results confirm that the proposed models adequately represent the real behavior of such systems under failure flows. Examples are provided to illustrate the GL-model construction process for both of the aforementioned system types.

The present study is concerned with the flow and heat transfer for laminar flow over an array of rectangular blocks. These blocks represent finite heat sources on parallel plates (e.g. the components of integrated circuits). The study is based on simulations of three and six aligned hot small blocks. The investigation aims to determine the effect of different parameters (e.g. Reynolds number and blocks arrangement) on the magnitude and location of the maximum temperature at the surfaces of the hot blocks. The numerical investigation was carried out using the commercial code ANSYS 5.4 based on the finite element technique. Also, experimental investigation was carried out for verification and more understanding of the problem. Neural networks were utilized to predict the values of the mean Nusselt number of the integrated circuit components. Useful discussions and fruitful comments are presented