139 results about "Huffman decoding" patented technology

A hardware accelerator for improving the decompression performance when decompressing data in Lempel-Ziv-Huffman compressed data format. The use of a Huffman encoding second stage in the popular and widely-used Lempel-Ziv-Huffman standard improves the compression ratio but complicates the decompression, because the Huffman encoding is applied selectively only to certain parts of the Lempel-Ziv tokens, and thus Huffman decoding must also be applied selectively during decompression. The present invention features a variable-length token decoder which is able to selectively decode the Huffman-encoded portions of the compressed data, and therefore enables high-performance decompression for compressed data having a very good compression ratio. Such an accelerator is well-suited for use in data processors which are to be loaded with pre-compressed data and software applications, particularly those employing flash memory.

Several code detectors in parallel simultaneously examine varying overlapping segments of a data stream containing variable length codes, referred to as a data window. The data window segments directly address memory structures within each of the code detectors without any previous logic stages. Each code detector is responsible for a range of code lengths, and ignores data window bits that are not relevant to its code length range. Each code detector outputs a possible result to a layer of logic that selects the possible result of the single code detector which contains result data corresponding to a variable length code in the data window.

The proposed technique uses basic properties of a Huffman codebook to decode a coded data bit stream having a plurality of variable length codewords based on the Huffman codebook. This is achieved by sorting codewords in the Huffman codebook based on potential values. The potential values are computed using the basic parameters of the codewords in the Huffman codebook. A current bit sequence having a predetermined length is extracted from the coded data bit stream. A potential value of the extracted bit sequence is then computed using the basic parameters of the codewords in the Huffman codebook. The sorted Huffman codebook is then searched to find a computed potential value in the sorted Huffman codebook that is substantially close to the computed potential value of the extracted bit sequence. The extracted current bit sequence is decoded based on the outcome of the search.

This invention discloses a hidden-writing method for JPEG lossless compressed images based on forecast coding, when inserting data in a JPFG lossless compressed image, it first of all carries Huffman decoding to get a forecast error, then inserts secret data in the forecast error and finally carries out Huffman coding to the modified forecast error to generate cryptograph image of the JPEG lossless coding, which utilizes modular arithmetic when inserting, which not only reduces alteration of data insertion to carrier images to keep a rather high image quality but also stores the inserted data either in the lossless compressed JPRG code streams or in the compressed cryptograph images.

This invention provides one Holman decoding method for JPEG codes flow, which comprises the following steps: a, according to the flow number list to establish minimum code sub list; b, according to the code number forming minimum code address list; c, adopting 0 to 16 bit comparer to get the Holman code relative RUN/SIZE list; d, according to SIZE value to restore codes front data. Comparing with current technique, this method forms different codes length minimum codes list and address list to lower the register array materials occupancy.

An adaptive canonical Huffman decoder including a symbol index generator, a content selector, and a symbol table buffer circuit is illustrated. The content selector outputs a content selection signal. The symbol table buffer circuit reads a corresponding symbol table from a plurality of symbol tables stored in an external memory according to the content selection signal and stores the corresponding symbol table. The symbol index generator stores decoding information of a plurality of encoding tables and selects a corresponding decoding information among all the decoding information according to the content selection signal. Then, the symbol index generator receives a bit stream and decodes the bit stream according to the corresponding decoding information to obtain a symbol index. After that, the symbol table buffer circuit obtains an output symbol from the corresponding symbol table according to the symbol index.

The invention provides a fractal-based binocular stereoscopic video compression and decompression method. In binocular stereoscopic video coding, a left channel is taken as a basic layer, a single motion compensation predictive mode (MCP) is adopted for coding, and the method comprises the following steps of: performing block DCT transformation coding on a left-eye start frame, performing motion estimation/compensation coding on a left-eye non-I frame, and calculating the pixel sum and the sum of squares of pixels of subblock domain and father block domain-related subblocks; searching the most similar matching block by using a full search method in a previous frame, namely a reference frame searching window of left-eye video; and finally compressing the coefficient of an iterated function system by using a Huffman coding method. A right channel is taken as an enhancement layer, the MCP and a parallax compensation predictive mode (DCP) are adopted for coding, and the lowest error is selected as a predicted result. During the DCP coding mode, the polalization and directionality in a stereoscopic parallel shooting structure are fully utilized; and the corresponding decompression process comprises the following steps of: for the left eye, decoding the start frame I by adopting a reverse DCT transformation mode, and performing Huffman decoding on the non-I frame so as to acquire the coefficient of the iterated function system; performing macrolbock-based decoding, calculating the pixel sum and the sum of squares of pixels of father block domain-related subblocks in the previous frame; and for a right eye, calculating the pixel sum and the sum of squares of pixels of the father block domain-related subblocks in the right-eye previous frame and a left-eye corresponding frame.

A method for realizing Huffman decoding includes receiving data stream being Huffman-coded, intercepting different cipher of data stream and using it as word to be decoded, comparing word to be decoded separately with address indexing table used to indicate code word position in Huffman code table to obtain corresponding Huffman code table address then obtaining data symbol before Huffman table by seeking Huffman table according to Huffman code table address and outputting obtained data symbol out. The device used for realizing said method is also disclosed.

The invention provides a quick fractal video compression and decompression method, comprising the following steps: firstly, coding an initial frame by block DCT (discrete cosine transformation); carrying out block movement estimation/ compensation coding on a non-I frame; calculating the pixel sum and the pix quadratic sum of a sub block relevant to a sub block region and a father block region; meanwhile, calculating the pixel sum and the pixel quadratic sum of the interpolated value corresponding block of a subpixel; then, carrying out preview search restricted condition judgment; finding the most similar matching block in a previous frame of search window by an unsymmetrical cross-shaped multi-level hexagon lattice point search algorithm matched and improved by a subpixel block; and finally, compressing an iterated function system coefficient by a Huffman coding method. The corresponding decompression process comprises the following steps: decoding the I frame in an inverse DCT mode; performing Huffman decoding on the non-I frame to obtain the iterated function system coefficient; then, decoding on the basis of a macroblock; calculating the pixel sum and the pixel quadratic sum of a relevant sub block in the father block region; then, successively decoding each macroblock in the current frame; and utilizing a deblocking loop filter method.

The invention provides a fractal-based video compression and decompression method. The compression method is a novel video compression and encoding method which adopts the fractal iteration principle for video encoding, and comprises the following steps: encoding a starting frame through block-DCT transformation, and carrying out block motion estimation/compensation coding for a non-I frame, which comprises calculating the sum and sum of squares of the pixels of sub-blocks related to sub-block domains and parent-block domains, finding a most similar matching block in a previous frame search window through full search, and finally compressing factors of an iteration function system through Huffman encoding. The corresponding decompression method comprises the following steps: decoding the I frame through anti-DCT transformation, carrying out Huffman decoding for the non-I frame to obtain the factors of the iteration function system, and carrying out macroblock-based decoding, which comprises calculating the sum and sum of squares of the pixels of the sub-blocks relevant to the parent-block domains, and decoding the macroblocks in the current frame in sequence. The method improves the traditional method of fractal video compression, and not only greatly increases the compression ratio and the peak signal-noise ratio, but also improves the encoding speed, thereby enhancing the fractal video compression and decoding performance, and making the fractal video compression and decoding more practical.

The invention discloses an image falsification detecting and falsification positioning method based on an image signature, which comprises the following steps: performing noise-depressing treatment on an original image; extracting image characteristic points by utilizing an adaptable Harris angular point detection algorithm; constructing a characteristic point neighborhood; calculating a statistic quantity in the characteristic point neighborhood, acquiring a characteristic vector value and generating a middle signature; performing Huffman coding, thereby acquiring the final signature of the original image; performing the Huffman decoding on the final signature of the original image, thereby acquiring the middle signature of the original image; generating the middle signature of a detected image; defining a distance between the two middle signatures and judging if the detected image is falsified; and positioning a falsified area. The method provided by the invention has excellent robustness to the image processing operations of visually acceptable geometric deformation and content retention such as JPEG (joint photographic experts group) compression, noise adding, filtering, and the like, has excellent sensitivity to the spite falsification attack, and can be used for confirming the position of the falsified area.

The present invention relates to a Huffman decoding method. Firstly, a Huffman table including multiple Huffman codes and multiple corresponding size/symbol codes is extracted. Each Huffman code comprises a codeword and each size/symbol code comprises a size code and a symbol code. Then, multiple mask codes are generated according to the size codes. Then, masking operations are performed on successive 16 bits of the compression bit stream and the mask codes, thereby outputting multiple masking results. Subsequently, logic operations on the masking results and the Huffman codes, thereby generating multiple are performed on the new Huffman codes, wherein each new Huffman code includes the codeword. It is discriminated whether the 16 bits of the compression bit stream complies with a specific one of new Huffman codes, thereby outputting the size/symbol code corresponding to the codeword.

The invention relates to a huffman fast decoding method based on the probability table look-up, comprising: reading the code stream in turn in the decoding, looking up the corresponding information in the supplementary table according to the read code stream, and outputting the corresponding character after coding based on the corresponding information, wherein the supplementary table is built according to the huffman table, comprising a plurality of groups of information after coding, and reflecting the probability of occurrence in the code stream of the corresponding character after coding. According to the invention, a supplementary table is built before the huffman decoding, one or a plurality of huffman codes are obtained through one replacement and the 16-bit table look-up under the condition of higher probability of occurrence of the coded character in the data stream, and the reading of the code stream uses word as the unit, while in the lower probability, a huffman code is obtained in the routine way, which advances the coding speed of the huffman.

The present invention relates to computing circuits and method for running an MPEG-2 AAC or MPEG-4 AAC algorithm efficiently, which is used as an audio compression algorithm in multi-channel high-quality audio systems, on programmable processors. In accordance with the present invention, the IMDCT process which takes large part of the amount of the operations in implementation of an MPEG-2/4 AAC algorithm can be performed in efficient. In addition, while the architecture of the existing digital signal processor is still used, the performance can be improved by means of the addition of the architecture of the address generator, Huffman decoder, and bit processing architecture. After all, to design and change the programmable processor is facilitated.

The invention provides a fractal-based multi-view three-dimensional video compression coding and decoding method. In the multi-view three-dimensional video coding, the intermediate view is selected as a reference view which is compressed by using a motion compensation prediction (MCP) principle, and other views are compressed by using a difference compensation prediction and motion compensation prediction-based (DCP+MCP) principle. Taking a three-view video for example, the intermediate view is used as the reference view, the independent MCP is used for coding, block discrete cosine transform (DCT) is used for coding an initial frame, block motion estimation/compensation coding is performed on non-I frames, the most similar matching block is searched from the previous frame, namely a reference frame search window of the intermediate-view video, by a full-search method, and finally the coefficient of an iterated function system is compressed by a Huffman coding method. The left view and the right view are coded by using the MCP mode and the DCP mode respectively; and when DCP coding is performed, polarization and directivity in a three-dimensional parallel camera structure are fully utilized. A corresponding decoding process comprises the following steps of: decoding the I frame by using an inverse DCT transform mode; performing Huffman decoding on the non-I frames to obtain the coefficient of the iterated function system; performing macro block-based decoding; calculating pixel sum and pixel quadratic sum of sub-blocks associated with a master block domain in the previous frame; and calculating the pixel sum and the pixel quadratic sum of the sub-blocks associated with the master block domains of the previous frame and the frame corresponding to the intermediate view for the left view and the right view respectively.

When data of one block is subjected to Huffman decoding (S100 to S120), inverse quantization processing is executed on the decoded data (S130), and when a present block is a starting point block, starting point restoring information (file pointer, decoding intermediate information and DC component) for restoring the image file with the block as a starting point is stored in a predetermined area of a data buffer (S140, S150). Then, inverse DCT calculation processing is carried out, color conversion processing is executed (S160, S170) and the histogram of RGB values is accumulated and stored in a predetermined area of the data buffer (S180).

The invention discloses a Huffman decoding method. The method comprises the following steps: receiving a code word to be decoded; comparing the code word to be decoded with a code word minimum value corresponding to each code length respectively; when the code word to be decoded is smaller than the code word minimum value corresponding to any code length in each code length, determining the effective code length of the code word to be decoded; and looking up a decoded code word corresponding to the effective code length from a corresponding relation between the code length and the decoded code word according to the effective code length to realize decoding. According to the Huffman decoding method provided by the embodiment of the invention, parallel code length acquisition can be realized in combination with a field programmable gate array (FPGA), so that the canonical form Huffman decoding efficiency is increased.