Adaptive Threshold Modulation For Error Diffusion Halftoning
& Bioassays Resources...DNA & RNABLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)E-UtilitiesGenBankGenBank: BankItGenBank: SequinGenBank: tbl2asnGenome WorkbenchInfluenza VirusNucleotide DatabasePopSetPrimer-BLASTProSplignReference Sequence (RefSeq)RefSeqGeneSequence Read Archive (SRA)SplignTrace ArchiveUniGeneAll DNA & RNA Resources...Data & SoftwareBLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)Cn3DConserved Domain Search Service (CD Search)E-UtilitiesGenBank: BankItGenBank: SequinGenBank: tbl2asnGenome ProtMapGenome WorkbenchPrimer-BLASTProSplignPubChem Structure SearchSNP Submission ToolSplignVector Alignment Search Tool (VAST)All Data & Software Resources...Domains & StructuresBioSystemsCn3DConserved Domain Database (CDD)Conserved Domain Search Service (CD Search)Structure (Molecular Modeling Database)Vector Alignment Search Tool (VAST)All Domains & Structures Resources...Genes & ExpressionBioSystemsDatabase of Genotypes and Phenotypes (dbGaP)E-UtilitiesGeneGene Expression Omnibus (GEO) Database Gene Expression Omnibus (GEO) DatasetsGene Expression Omnibus (GEO) ProfilesGenome WorkbenchHomoloGeneMap ViewerOnline Mendelian Inheritance in Man (OMIM)RefSeqGeneUniGeneAll Genes & Expression Resources...Genetics & MedicineBookshelfDatabase of Genotypes and Phenotypes (dbGaP)Genetic Testing RegistryInfluenza VirusMap ViewerOnline Mendelian Inheritance in Man (OMIM)PubMedPubMed Central (PMC)PubMed Clinical QueriesRefSeqGeneAll Genetics & Medicine Resources...Genomes & MapsDatabase of Genomic Structural Variation (dbVar)GenBank: tbl2asnGenomeGenome ProjectGenome ProtMapGenome WorkbenchInfluenza VirusMap ViewerNucleotide DatabasePopSetProSplignSequence Read Archive (SRA)SplignTrace ArchiveAll Genomes & Maps Resources...HomologyBLAST (Basic Local Alignment Search Tool)BLAST (Stand-alone)BLAST Link (BLink)Conserved Domain Database (CDD)Conserved Domain Search Service
input color signal to a boundary separating available output colors. The input color signal is adjusted, at least in part, to reflect a relationship between the output color and the input color signal for another pixel. The boundary is adjusted,...https://www.google.ch/patents/US6707576?utm_source=gb-gplus-sharePatent US6707576 - Noise modulation error diffusion of digital halftoning Erweiterte PatentsucheTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents. VeröffentlichungsnummerUS6707576 B1PublikationstypErteilung AnmeldenummerUS 09/453,324 Veröffentlichungsdatum16. März 2004Eingetragen2. Dez. 1999 Prioritätsdatum2. Dez. 1999GebührenstatusBezahlt Veröffentlichungsnummer09453324, 453324, US 6707576 B1, US 6707576B1, US-B1-6707576, US6707576 B1, US6707576B1 ErfinderWilliam H. ChangUrsprünglich BevollmächtigterSharp Laboratories Of America, Inc.Zitat exportierenBiBTeX, EndNote, RefManPatentzitate (37), http://www.ncbi.nlm.nih.gov/pubmed/18249601 Nichtpatentzitate (25), Referenziert von (13), Klassifizierungen (13), Juristische Ereignisse (6) Externe Links:USPTO, USPTO-Zuordnung, EspacenetNoise modulation error diffusion of digital halftoning US 6707576 B1 Zusammenfassung An output color is selected for a subject pixel by comparing an input color signal to a boundary separating available output colors. The input color signal is adjusted, at least in part, to reflect a https://www.google.ch/patents/US6707576 relationship between the output color and the input color signal for another pixel. The boundary is adjusted, at least in part, to reflect a change in the magnitude of a noise source. Bilder(7) Ansprüche(29) What is claimed is: 1. A method for selecting an output color for a subject pixel comprising, (a) comparing an input color signal to a boundary separating available output colors; (b) adjusting said input color signal, at least in part, to reflect a relationship between said output color of a previous pixel and said input color signal of a previous pixel for another pixel; and (c) adjusting said boundary, at least in part, to reflect a change in a magnitude of a Guassian like noise. 2. The method of claim 1 wherein said boundary is further adjusted as a function of said input color signal. 3. The method of claim 1 wherein said boundary is further adjusted as a function of a luminance level of said input color signal. 4. The method of claim 1 wherein said relationship is a difference betw
Niranjan Damera-Venkata (HP Labs) and Dr. Thomas D. Kite (Audio Precision) Graduate Students: Mr. Vishal Monga Other Collaborators: Prof. Alan C. Bovik (UT Austin) and Prof. Wilson S. Geisler (UT Austin) Talk in http://users.ece.utexas.edu/~bevans/projects/halftoning/talks/ErrorDiffusion.html Powerpoint and PDF formats Halftoning Research at UT Austin - Halftoning Toolbox Abstract Image halftoning converts a high-resolution image to a low-resolution image, e.g. a 24-bit color image to a three-bit color image or an 8-bit grayscale image to a binary image, for printing and display. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to adaptive threshold the input in order to diffuse the error among neighboring grayscale pixels. Grayscale error diffusion introduces nonlinear distortion (directional artifacts and false textures), linear distortion (sharpening), and additive noise. We describe approaches to compensate linear and nonlinear distortion based on observation that error diffusion is 2-D sigma-delta modulation (Anastassiou, 1989). Following the 1-D sigma-delta work of Ardalan and Paulos (1988), we replace the adaptive threshold modulation thresholding quantizer with a scalar gain plus additive noise. The amount of sharpening is proportional to the scalar gain. Setting the sharpness control parameter in the threshold modulation approach of Eschbach and Knox (1991) can theoretically eliminate sharpening effects. We use unsharpened halftones in perceptually weighted SNR measures. We also use the sharpness control parameter to achieve rate-distortion tradeoffs in JBIG2 compression of error diffused halftones. We generalize the approach for linear distortion compensation by using an adaptive threshold modulation framework. Using the framework, we adaptively optimize the hysteresis coefficients in green noise error diffusion of Levien (1993). For edge enhancement halftoning, we minimize linear distortion by adapting the sharpness control parameter. We break up directional artifacts by using a deterministic bit flipping quantizer, which was used by Magrath and Sandler (1997) in sigma-delta research. Finally, we generalize our work to vector error diffusion (Haneishi et al. 1993) for color images. The scalar gain becomes a matrix gain. We apply an adaptive framework to optimize visual quality by using a linear color vision model. We evaluate four linear color vision models via subjective testing. We rece
be down. Please try the request again. Your cache administrator is webmaster. Generated Fri, 30 Sep 2016 02:18:20 GMT by s_hv987 (squid/3.5.20)