Showing papers on "Run-length encoding published in 2001"

Data compression having more effective compression

Abstract: A lossless data compression system comprises a content addressable memory dictionary (30), a coder (38), and a run length encoding means (39) connected to receive the output of the coder (38), the encoding means (39) being arranged to count the number of times a match consecutively occurs at a predetermined dictionary location, i.e. the number of times the same search tuple is loaded into the same address (50) of the dictionary. Compression is improved.

Image data compression

Abstract: Two-dimensional blocks of line-work image data are subjected to: fractal reordering; run length encoding of the pixel values of the fractal re-ordered data, index encoding of the run length encoded pixel values; and entropy encoding of the index encoded pixel values and run lengths. This lossless, but in case of applying a quantisation step, lossy image data compression and decompression method is particularly suitable for handling image data in a printer environment, and more particularly for handling image data comprising line work data or both raster images and line work data.

Method and data structure for compressing file-reference information

Abstract: Compression of local data uses various methods to encode location data (e.g. line numbers and, optionally, column numbers) representing references to a source code information symbol in a source code file. Run length encoding and other run encoding methods are used. A preferred run encoding method encodes a run-encoded bitmap comprising a set of runs, in which each run has a first run field that provides a starting line for the run, and a second run field that encodes as a bitmap the remaining lines in the run in which the reference appears. A run length field may be used to indicate the length of the second run field. Another run field may be used to encode the length of the first run field, to permit further compression.

Improving Raster Image Run-Length Encoding Using Data Order

Abstract: We examine the technique of run-length encoding in combination with data order, where our attention is focused on good performance of image operations such as, e.g., rotation, reflection, and zooming. To this end we develop a new type of data order that supports these operations well and allows to perform them on a variant of a double-queue automaton directly on the compressed data stream. Because of its shape we call this data order shamrock or S-order. To confirm our theoretical results on S-order we have performed some experiments on sample data using various data orderings that appear in the literature.

Split image data linking processor, method and its recording medium

Abstract: PROBLEM TO BE SOLVED: To provide an image processor, method and its recording medium capable of exactly processing data at a boundary part as well and reducing a load to be applied to an image processing by performing data compression to split image data and properly linking the boundary part generated by the splitting. SOLUTION: An image processor 1 as one embodiment of this invention is constituted of an A/D conversion part 1a to perform A/D conversion to an inputted image signal, a binary processing part 1b to binary process the image data outputted from 1a, a run length encoding part 1c to make binary data outputted from 1b into run length codes, a data memory 1d to temporarily store the run length encoded data outputted from 1c, a linkage processing part 1e to link the run length encoded data stored in 1d and a linked data memory 1f to store the run length encoded data linked by 1e.

Space-color quantization of multispectral images in hierarchy of scales

Abstract: In this paper a novel model for multiscale space-color quantization of multispectral images, is described. The approach is based on the hierarchical clustering technique, derived from the statistical physics model of free energy (Jovovic 1996, Jovovic et al. 1999). The group vectors for image color are computed on the adaptively selected windows of computation, as contrasted to the block-size windows, optimizing the accuracy of the computation of the group vectors with the density of sampling an image by the group windows. The algorithm is suitable for implementation in parallel computer architectures. The results of quantization of color images by our algorithm are compared with 3 image compression techniques: 1) wavelets, 2) discrete cosine transform (DCT), and, 3) quad tree (QT). Contextual information of spatial coherency of the data is used in the segmentation process, in our algorithm. As a result, much better spatial resolution and small size of compressed images are obtained by our algorithm, as compared to the other techniques, for any error level of compression selected. Major spatial features are optimally color-coded along the hierarchy of scales of computation. The images quantized with our algorithm are suitable for the run-length encoding scheme of the hierarchy of binary images.

Pitman Shorthand inspired model for plain text compression

Abstract: With the growing demand for text transmission and storage due to advent of Internet technology, text compression has gained its own momentum. Generally text is coded in ASCII format. Huffman coding or any other run length encoding techniques compresses the plain text. We propose a new method for plain text compression, which is mainly inspired by the concepts of Pitman Shorthand. The coding scheme successfully codes a group of successive 2-3 text characters into a single code. In this paper we have illustrated the implementation of the plain text compression using a new coding scheme, which is based on the concepts of Pitman Shorthand. Further application of Huffman coding on the codes generated is possible and is expected to result in a greater compression.