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Timestamp: 2014-07-24 05:18:27
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Patent US6738520 - Method of compressing an image - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsSeveral embodiments in accordance with the invention are disclosed. An embodiment for compressing a gray scale image is described and an embodiment for decompressing the codebook produced by compressing the gray scale image is described. Of course, the invention is not limited to only gray scale images,...http://www.google.com/patents/US6738520?utm_source=gb-gplus-sharePatent US6738520 - Method of compressing an imageAdvanced Patent SearchPublication numberUS6738520 B1Publication typeGrantApplication numberUS 09/597,354Publication dateMay 18, 2004Filing dateJun 19, 2000Priority dateJun 19, 2000Fee statusPaidAlso published asUS20040071350Publication number09597354, 597354, US 6738520 B1, US 6738520B1, US-B1-6738520, US6738520 B1, US6738520B1InventorsTinku Acharya, Bhargab Bikram Bhattacharya, Malay Kumar Kundu, Suman Kumar Mitra, Chivukula A. MurthyOriginal AssigneeIntel CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (45), Non-Patent Citations (64), Referenced by (7), Classifications (4), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetMethod of compressing an imageUS 6738520 B1Abstract Several embodiments in accordance with the invention are disclosed. An embodiment for compressing a gray scale image is described and an embodiment for decompressing the codebook produced by compressing the gray scale image is described. Of course, the invention is not limited to only gray scale images, however.
BACKGROUND The present disclosure is related to compressing images.
One technique that is sometimes employed in compression, such as in connection with the wavelet transform, or fractal-based compression/decompression, as two examples, involves a multi-resolution decomposition or multi-level division of the image. An embodiment of the present invention, based at least in part on fractal image compression, using an Iterative Function System (IFS), guided by certain probability measures, shall be described in more detail hereinafter. A basic notion of fractal image compression lies in the usage of self-similarity embedded in a given image. Using a set of affine contractive maps, as shall be explained in more detail hereinafter, for example, portions of an image may be reproduced, and by taking a collage of these parts, the entire image may be regenerated. The set of affine contractive maps is referred to in this context as IFS. For a particular embodiment in accordance with the invention, although the invention is not limited in scope in this respect, probabilities are assigned to the maps, which in turn, affect the iteration process, and generate, in this embodiment, a close approximation of the image, employing a manageable amount of computational complexity or computational burden. A way to conceptualize the multi-level division of the image, then, for this particular embodiment, is as a �divide-and-conquer� approach. Therefore, an image may be subdivided and then the subdivisions may be further subdivided in a recursive fashion to determine how to compress the image efficiently.
Σp K=1, [2] where K=1 to 4.
Once an estimate, IN+1, equals the image from which it is estimated, IN, for each quadrant of the estimate image, IN, low variation quadrants are estimated using mean gray scale values. If the particular quadrant is then �close� to the corresponding quadrant of the original image, I, those quadrants are stored as either the mean gray scale value of the original image, if it was a low variation quadrant, or the probability value for the original image, if it was a high variation quadrant. If all quadrants of the estimate image are close to the original image, compression is complete. However, if some quadrants of the estimate are not close, then those or that quadrant may not be estimated using a mean gray scale value or a contractive map of the image along with the probability (pK) at this particular stage. Therefore, the previous approach, beginning with subdivision into quadrants, is now applied to that or those quadrants of the original image, and that or those quadrants of the original image are treated as described above. In other words, the quadrant or quadrants are treated as the original image in the previous description, divided into four quadrants, and so forth.
Blocks 170 and 180 refer to an approach where the estimate of the image is formed from the estimate of the quadrants, the previous calculations are repeated and so forth, until two successive image estimates are equal. Once two successive image estimates are equal, low variation quadrants are estimated using mean gray scale values at block 185. At block 190, the quadrants of the estimate are compared with the quadrants of the original image. At 195, if all of the estimated quadrants are close to the original quadrants, then the entire image is estimated using mean gray scale values and/or probability values, depending on whether a particular quadrant is a low variation quadrant or a high variation quadrant. It is noted, in this context, that any one of a number of different measures may be employed to determine whether the estimate of the quadrant and the corresponding quadrant of the original estimate are �close,� such as mean squared error (MSE) and other approaches. If on the other hand, not all the quadrant estimates are close to the corresponding original image quadrants, then for those quadrants that are close, the estimate of the quadrant is employed, whether the mean or the probability for the particular quadrant and, for those quadrants that are not close, the particular quadrant is treated as an image and is subdivided, at block 120, and so forth. It is noted in this context that for this particular embodiment a quadrant which is estimated and not close to the original image would probably be a high variation quadrant, due to the presence of block 185.
This embodiment begins with an arbitrary image, J, having the same dimensions as the image to be decompressed, this arbitrary image having at least one zero pixel gray scale value. Therefore, at block 210, this image is read or input and the �codebook� of the compressed image is read or input. At block 220, the mean or average value for the compressed image is read from the codebook. At 230, the average value of the arbitrary image is computed. At 240, the arbitrary image is divided into quadrants. At 250, 260, 270, and 280, if the code for each quadrant from the codebook is a mean value, mK, then the image is decompressed using these values as indicated in block 270, and decompression is complete.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS4941193 *Oct 2, 1987Jul 10, 1990Iterated Systems, Inc.Methods and apparatus for image compression by iterated function systemUS5065447 *Jul 5, 1989Nov 12, 1991Iterated Systems, Inc.Method and apparatus for processing digital dataUS5347600 *Oct 23, 1991Sep 13, 1994Interated Systems, Inc.Method and apparatus for compression and decompression of digital image dataUS5416856 *Mar 30, 1992May 16, 1995The United States Of America As Represented By The Secretary Of The NavyMethod of encoding a digital image using iterated image transformations to form an eventually contractive mapUS5592228Mar 2, 1994Jan 7, 1997Kabushiki Kaisha ToshibaVideo encoder using global motion estimation and polygonal patch motion estimationUS5600731May 9, 1991Feb 4, 1997Eastman Kodak CompanyMethod for temporally adaptive filtering of frames of a noisy image sequence using motion estimationUS5724451 *Dec 27, 1994Mar 3, 1998Samsung Electronics Co., Ltd.Image compression method and apparatus using conditional quadtree split sysyemUS5740282 *Jun 30, 1995Apr 14, 1998Iterated Systems, Inc.System and method for contractive mapping resynchronization of a data transmissionUS5778098Mar 22, 1996Jul 7, 1998Microsoft CorporationFor encoding information in video image frame sequence in compressed formatUS5832115Jan 2, 1997Nov 3, 1998Lucent Technologies Inc.Ternary image templates for improved semantic compressionUS5862262 *Mar 30, 1992Jan 19, 1999The United States Of America As Represented By The Secretary Of The NavyMethod of encoding a digital image using adaptive partitioning in an iterated transformation systemUS5867221 *Mar 29, 1996Feb 2, 1999Interated Systems, Inc.Method and system for the fractal compression of data using an integrated circuit for discrete cosine transform compression/decompressionUS5875122Dec 17, 1996Feb 23, 1999Intel CorporationIntegrated systolic architecture for decomposition and reconstruction of signals using wavelet transformsUS5978030May 5, 1995Nov 2, 1999Daewoo Electronics Co., Ltd.Method and apparatus for encoding a video signal using feature point based motion estimationUS5982441 *Jan 12, 1996Nov 9, 1999Iterated Systems, Inc.System and method for representing a video sequenceUS5995210Aug 6, 1998Nov 30, 1999Intel CorporationIntegrated architecture for computing a forward and inverse discrete wavelet transformsUS6009201Jun 30, 1997Dec 28, 1999Intel CorporationEfficient table-lookup based visually-lossless image compression schemeUS6009206Sep 30, 1997Dec 28, 1999Intel CorporationCompanding algorithm to transform an image to a lower bit resolutionUS6009210Mar 5, 1997Dec 28, 1999Digital Equipment CorporationHands-free interface to a virtual reality environment using head trackingUS6044168Nov 14, 1997Mar 28, 2000Texas Instruments IncorporatedModel based faced coding and decoding using feature detection and eigenface codingUS6047303Aug 6, 1998Apr 4, 2000Intel CorporationSystolic architecture for computing an inverse discrete wavelet transformsUS6091851Nov 3, 1997Jul 18, 2000Intel CorporationEfficient algorithm for color recovery from 8-bit to 24-bit color pixelsUS6094508Dec 8, 1997Jul 25, 2000Intel CorporationPerceptual thresholding for gradient-based local edge detectionUS6108453Sep 16, 1998Aug 22, 2000Intel CorporationGeneral image enhancement frameworkUS6124811Jul 2, 1998Sep 26, 2000Intel CorporationReal time algorithms and architectures for coding images compressed by DWT-based techniquesUS6130960Nov 3, 1997Oct 10, 2000Intel CorporationBlock-matching algorithm for color interpolationUS6151069Nov 3, 1997Nov 21, 2000Intel CorporationDual mode digital camera for video and still operationUS6151415Dec 14, 1998Nov 21, 2000Intel CorporationAuto-focusing algorithm using discrete wavelet transformUS6154493May 21, 1998Nov 28, 2000Intel CorporationCompression of color images based on a 2-dimensional discrete wavelet transform yielding a perceptually lossless imageUS6157747Aug 1, 1997Dec 5, 2000Microsoft Corporation3-dimensional image rotation method and apparatus for producing image mosaicsUS6166664Aug 26, 1998Dec 26, 2000Intel CorporationEfficient data structure for entropy encoding used in a DWT-based high performance image compressionUS6178269Aug 6, 1998Jan 23, 2001Intel CorporationArchitecture for computing a two-dimensional discrete wavelet transformUS6195026Sep 14, 1998Feb 27, 2001Intel CorporationMMX optimized data packing methodology for zero run length and variable length entropy encodingUS6215908Feb 24, 1999Apr 10, 2001Intel CorporationSymmetric filtering based VLSI architecture for image compressionUS6215916Feb 4, 1998Apr 10, 2001Intel CorporationEfficient algorithm and architecture for image scaling using discrete wavelet transformsUS6229578Dec 8, 1997May 8, 2001Intel CorporationEdge-detection based noise removal algorithmUS6233358Jul 13, 1998May 15, 2001Intel CorporationImage compression using directional predictive coding of the wavelet coefficientsUS6236433Sep 29, 1998May 22, 2001Intel CorporationScaling algorithm for efficient color representation/recovery in videoUS6236765Aug 5, 1998May 22, 2001Intel CorporationDWT-based up-sampling algorithm suitable for image display in an LCD panelUS6269181Jan 13, 2000Jul 31, 2001Intel CorporationEfficient algorithm for color recovery from 8-bit to 24-bit color pixelsUS6275206Mar 17, 1999Aug 14, 2001Intel CorporationBlock mapping based up-sampling method and apparatus for converting color imagesUS6285796Nov 3, 1997Sep 4, 2001Intel CorporationPseudo-fixed length image compression schemeUS6292114Jun 10, 1999Sep 18, 2001Intel CorporationEfficient memory mapping of a huffman coded list suitable for bit-serial decodingUS6301370Dec 4, 1998Oct 9, 2001Eyematic Interfaces, Inc.Face recognition from video imagesUS6516093Apr 30, 1997Feb 4, 2003Koninklijke Philips Electronics N.V.Segmented video coding and decoding method and system* Cited by examinerNon-Patent CitationsReference1"Image Sequence Coding at Very Low Bitrates: A Review", H. Li, et al., 1994 IEEE, 11 pages.2"Local Motion Tracking in Semantic-Based Coding of Videophone Sequences", IEEE, 1997, P.M. Antoszczyszyn, et al., 3 pages.3Acharay, "A Block-Matching Algorithm For Color Interpolation", Application No. 09/494,087, Filed: Jan. 28, 1999, Attorney Docket No. 042390.P5090D, 45 Pgs.4Acharya, "A Mathematical Model for Gray Scale and Contrast Enhancement of a Digital Image", Application No. 09/207,753, Filed: Dec. 8, 1998, Attorney Docket No. 042390.P6217, 28 Pgs.5Acharya, "A Median Computation-Base Integrated Color Interpolation and Color Space Conversion Methodology from -Bit Bayer Pattern RGB Color Space to 24-Bit CIE YXZ Color Space", Application No. 09/050,743, Filed Mar. 30, 1998, Attorney Docket No. 042390.P5045, 36 Pgs.6Acharya, "A Median Computation-Based Integrated Color Interpolation and Color Space Conversion Methodology from 8-Bit Bayer Pattern RGB Color Space to 12-Bit YCrCb Color Space".7Acharya, "A Memory Based VLSI Architecture for Image Compression", Application No. 09/885-415, Filed Jun. 30, 1997, Attorney Docket No. 042390.P4425, 37 Pgs.8Acharya, "An Efficient Methodology to Select the Quantization Threshold Parameters in a DWT-Based Image Compression Scheme in Order to Store a Predefined Minimum Number of Images into a Fixed Size Secondary Storage".9Acharya, "An Integrated Color Interpolation and Color Space Conversion Algorithm From 8-Bit Bayer Pattern RGB Color Space to 12-Bit YCrCb Color Space", Application No. 09/034,625, Filed Mar. 4, 1998, Attorney Docket No. 042390.P4818, Pgs. 1-42.10Acharya, "An Integrated Color Interpolation and Color Space Conversion Algorithm from 8-Bit Bayer Pattern RGB Color Space to 24-Bit CIE XYZ Color Space", Application No. 09/048,901, Filed Mar. 26, 1998, Attorney Docket No. 042390.P5644, 43 Pgs.11Acharya, "Discrete Filter", Application No. 09/432,337, Filed: Sep. 2, 1999, Attorney Docket No. 042390.P7626, 12 Pgs.12Acharya, "Enhancing Image Compression Performance by Morphological Processing", Application No. 09/291,810, Filed: Apr. 14, 1999, Attorney Docket No. 042390.P6878, 31 Pgs.13Acharya, "Method of Converting A Sub-Sampled Color Image", Application No. 09/461,068, Filed: Dec. 14, 1999, Attorney Docket No. 042390.P4790, 23 Pgs.14Acharya, "Method of Upscaling a Color Image", Application No. 09/461,080, Filed: Dec. 14, 1999, Attorney Docket No. 042390.P7489, 22 Pgs.15Acharya, et al., "A Fuzzy Based Thresholding Technique for Image Segmentation", Application No. 09/393,136, Filed: Sep. 10, 1999, Attorney Docket No. 042390.P7114, 28 Pgs.16Acharya, et al., "A Fuzzy Distinction Based Thresholding Technique for Image Segmentation", Application No. 09/393,017 Filed: Sep. 10, 1999, Attorney Docket No. 042390.P7115, 29 Pgs.17Acharya, et al., "A New Scaling Algorithm and Architecture for Integer Scaling in Video", Application No. 09/008,131, Filed Jan. 16, 1998, Attorney Docket No. 042390.P4713, 38 Pgs.18Acharya, et al., "An Edge Enhanced Image Up-Sampling Algorithm Using Discrete Wavelet Transform", Application No. 09/292,763, Filed: Apr. 14, 1999, Attorney Docket No. 042390.P6879, 32 Pgs.19Acharya, et al., "An Efficient Companding Algorithm Suitable For Color Imaging", Application No. 09/482,551, Attorney Docket No. 042390.P4961D, 44 Pgs.20Acharya, et al., "Chip Rate Selectable Square Root Raised Cosine Filter For Mobile Telecommunications", Application No. 09/467,487, Filed: Dec. 20, 1999, Attorney Docket No. 042390.P8026, 33 Pgs.21Acharya, et al., "Color Interpolation for a Four Color Mosaic Pattern", Application No. 09/199,836, Filed: Nov. 24, 1998, Attorney Docket No. 042390.P6376, 26 Pgs.22Acharya, et al., "Image Processing Method and Apparatus", Application No. 09/359,523, Filed: Jun. 4, 1999, Attorney Docket No. 042390.P7323, 16 Pgs.23Acharya, et al., "Indexing Wavelet Compressed Video for Efficient Data Handling", Application No. 09/438,091, Filed: Nov. 10, 1999, Attorney Docket No. 042390.P6454, 29 Pgs.24Acharya, et al., "Mehtod Of Integrating a Watermark Into an Image", Application No. 09/519,874, Filed: Mar. 6, 2000, Attorney Docket No. 042390.P7136, 27 Pgs.25Acharya, et al., "Method of Compressing a Color Image", Application No. 09/411,697, Filed: Oct. 1, 1999, Attorney Docket No. 042390.P7463, 26 Pgs.26Acharya, et al., "Method of Compressing an Image", Application No. 09/597,354, Filed: Jun. 19, 2000, Attorney Docket No. 042390.P8760, 23 Pgs.27Acharya, et al., "Method of Integrating a Watermark Into a Compressed Image", Application No. 09/519,135, Filed: Mar. 6, 2000, Attorney Docket No. 042390.P7319, 25 Pgs.28Acharya, et al., "Method of Interpolating Color Pixel Signals From a Subsampled Color Image", Application No. 09/410,800, Filed: Oct. 1, 1999, Attorney Docket No. 042390.P7331, 20 Pgs.29Acharya, et al., "Method of Inverse Quantizing Quantized Signal Samples of an Image During Image Decompression", Application No. 09/507,213, Filed: Feb. 18, 2000, Attorney Docket No. 042390.P8350, 32 Pgs.30Acharya, et al., "Method of Quantizing Signal Samples of an Image During Image Compression", Application No. 09/507,399, Filed: Feb. 18, 2000, Attorney Docket No. 042390.P7135, 24 Pgs.31Acharya, et al., "Method of Using Hue to Interpolate Color Pixel Signals", Application No. 09/591,867, Filed: Jun. 12, 2000, Attorney Docket No. 042390.P8746, 23 Pgs.32Acharya, et al., "Square Root Raised Cosine Symmetric Filter for Mobile Telecommunications", Application No. 09/429,058, Filed: Sep. 2, 1999, Attorney Docket No. 042390.P7629, 26 Pgs.33Acharya, et al., "Video Motion Estimation", Application No. 09/406,032, Filed: Sep. 27, 1999, Attorney Docket No. 042390.P7330, 24 Pgs.34Acharya, et al., "Zerotree Encoding of Wavelet Data", Application No. 09/390,255, Filed: Sep. 3, 1999, Attorney Docket No. 042390.P7057, 29 Pgs.35Application No. 09/040,806, filed Mar. 18, 1998, Attorney Docket No. 042390.P5657, 42 Pgs.36Application No. 09/146,159, Filed Sep. 3, 1998, Attorney Docket No. 042390.P6081, 35 Pgs.37Cai, et al., "Several Key Problems in Model-Based Image Sequence Compression by Using Interframe AUs Correlation", IEEE 1994, pp. 409-413.38Chang Choi, et al., "Analysis and Synthesis of Facial Image Sequences in Model-Based Image Coding", IEEE Transactions on circuits and systems or video technology, vol. 4, No. 3, Jun. 1994, pp. 257-275.39Chung-Lin Haung et al., "Human Facial Feature Extraction For Face Interpretation and Recognition", Pattern Recognition, vol. 25, No. 12, 1992, pp. 1435-1444.40D.J. Burr, "Elastic Matching of Line Drawings", IEEE Transactions on pattern anylysis and machine intellegence, vol. PAMI-3, No. 6, Nov. 1981, pp. 708-713.41Demetri Terzopoulos, et al., "Constraints on Deformable Models: Recovering 3D Shape and Nonrigid Motion", Schlumberger Palo Research, Palo Alto, CA, Artifical Intelligence 36, 1988, Elsevier Science Publishers B.V., pp. 91-123.42Dunton, et al., "Dual Mode Digital Camera for Video and Still Operation", Application No. 09/595,055, Filed: Jun. 16, 2000, Attorney Docket No. 042390.P5079C, 27 Pgs.43Edward J. Bawolek et al., "Infared Correction System", Application No. 09/126,203, Filed: Jul. 30, 1998, Attorney Docket No. 042390.P6172, 18 Pgs.44Gozde Bozdagi, et al., "3-D Motion Estimation and Wireframe Adaptation Including Photometric Effects for Model-Based Coding of Facial Image Sequence", IEEE Transactions on circuits and systems for video technology, vol. 4, 1994, pp. 246-256.45Gozde Bozdagi, et al., "Simultaneous 3-D Motion Estimation and Wire-Frame Model Adaptation Including Photometric Effects Fro Knowledge-Based Video Coding", Electrical and Electronics Eng. Dept. Bilkent University, Ankara, Turkey, 1994 IEEE, pp. 413-416.46Haibo Li, et al., "3-D Motion Estimation in Model-Based Facial Image Coding", IEEE Transactions on pattern analysis and machine intelligence, vol. 15 No. 6, Jun. 1993, pp. 545-555.47Haibo Li, et al., "Recursive Estimation of Facial Expression and Movement", Image Coding Group, Department of Electrical Engineering, Linkoping University, Linkoping, Sweden, 1992 IEEE, pp. 593-596.48John F.S. Yau, et al., "A Texture Mapping Approach to 3-D Facial Image Synthesis", North-Holland Computer Graphics Forum 7 1988, pp. 129-134.49Kim, et al., "Method of Performing Motion Estimation", Application No. 09/596,127, Filed: Jun. 16, 2000, Attorney Docket No. 042390.P8747, 29 Pgs.50Marco La Cascia, et al., "Head Tracking Via Robust Registration in Texture Map Images", Computer Science Department, Boston University, Boston, MA, 1998 IEEE, pp. 508-514.51Marie Dudon, et al., "Motion Estimation and Triangular Active Mesh", XP 002036860, pp. 47-53.52Metz, et al., "Image Processing Architecture", Application No. 09/473,643, Filed: Nov. 18, 1999, Attorney Docket No. 042390.P8050, 12 Pgs.53Miao, et al., "Dual Mode Filter for Mobile Telecommunication", Application No. 09/467,611, Filed: Nov. 18, 1999, Attorney Docket No. 042390.P8027, 31 Pgs.54Pazmino, et al., "Method of Compressing And/Or Decompressing a Data Set Using Significance Mapping", Application No. 09/151,336, Filed: Sep. 11, 1998, Attorney Docket No. 042390.P6391, 25 Pgs.55Shih-Ping Liou, "Model Based Motion Estimation Using Constrained Weighted Least Squares", Imaging Department, Siemens Corporation Research, Princeton, NJ, 1994 IEEE, pp. 810-812.56Tan, et al., "A Hardware Efficient Wavelet-Based Video Compression Scheme", Application No. 09/342,863, Filed: Jun. 29, 1999, Attorney Docket No. 042390.P6529, 32 Pgs.57Tan, et al., "A Methodology for Color Correction With Noise Regulation", Application No. 09/359,831, Filed: Jul. 23, 1999, Attorney Docket No. 042390.P7320, 30 Pgs.58Tan, et al., "Hi-Speed Deterministic Approach In Detecting Defective Pixels Within an Image Sensor", Application No. 09/258,636 Filed: Feb. 26, 1999, Attorney Docket No. 042390.P5434, 32 Pgs.59Tan, et al., "Method and Apparatus for Adaptively Sharpening Local Image Content of an Image", Application No. 09/328,935, Filed: Jun. 9, 1999, Attorney Docket No. 042390.P7125, 29 Pgs.60Tan, et al., "Method and Apparatus for Automatic Focusing in an Image Capture System Using Symmetric Fir Filters", Application No. 09/383117, Filed: Aug. 25, 1999, Attorney Docket No. 042390.P6694, 28 Pgs.61Tan, et al., "Reduction of Ringing Artifacts After Decompression of a DWT-Based Compressed Image", Application No. 09/165,511, Filed: Oct. 2, 1998, Attorney Docket No. 042390.P6220, 20 Pgs.62Tan, et al., "Robust Sequential Approach in Detecting Defective Pixels Within an Image Sensor", Application No. 09/191,310, Filed: Nov. 13, 1998, Attorney Docket No. 042390.P6575, 35 Pgs.63Tan, et al., "Using An Electronic Camera to Build a File Containing Text", Application No. 09/301,753, Filed: Apr. 29, 1998, Attorney Docket No. 042390.P5742, 21 Pgs.64Tsai, et al., "Method and Apparatus for Adaptively Sharpening an Image", Application No. 09/320,192, Filed: May 26, 1999, Attorney Docket No. 042390.P6452, 27 Pgs.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS6870961 *Nov 6, 2001Mar 22, 2005Ricoh Company, Ltd.Image decompression from transform coefficientsUS6982661Mar 18, 2003Jan 3, 2006Intel CorporationMethod of performing huffman decodingUS6987469Jun 3, 2003Jan 17, 2006Intel CorporationMethod of generating Huffman code length informationUS6999629 *May 24, 2000Feb 14, 2006France Telecom And Telediffusion De FranceImage coding/decoding methodUS7003169 *May 22, 2000Feb 21, 2006France TelecomVariable transformation hierarchical image codingUS7181077Jan 4, 2005Feb 20, 2007Ricoh Company, Ltd.Image decompression from transform coefficientsUS7190287Dec 6, 2005Mar 13, 2007Intel CorporationMethod of generating Huffman code length information* Cited by examinerClassifications U.S. Classification382/232International ClassificationG06T9/00Cooperative ClassificationG06T9/001European ClassificationG06T9/00FLegal EventsDateCodeEventDescriptionSep 21, 2011FPAYFee paymentYear of fee payment: 8Nov 26, 2007REMIMaintenance fee reminder mailedNov 19, 2007FPAYFee paymentYear of fee payment: 4Aug 3, 2004CCCertificate of correctionSep 11, 2000ASAssignmentOwner name: INDIAN STATISTICAL INSTITUTE, INDIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BHATTACHARYA, BHARGAB BIKRAM;KUNDU, MALAY KUMAR;MITRA, SUMAN KUMAR;AND OTHERS;REEL/FRAME:011100/0326Effective date: 20000808Owner name: INTEL CORPORATION, CALIFORNIAFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:ACHARYA, TINKU;REEL/FRAME:011076/0689Effective date: 20000721RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google