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Storer Michael W. Marcellin" ], "editors": [ {"first" : "James A. Storer Michael W.", "last" : "Marcellin"} ], "pages": "13-21","abstract": "JPEG 2000 part 9, or short JPIP, is an interactive image browsing protocol that allows the selective delivery of image regions, components or scales from JPEG 2000 image. Typical applications are browsing tools for medical databases where transmitting huge images from server to client in total would be uneconomical. Instead, JPIP allows extracting only the desired image parts for analysis by an http type request syntax. Such a JPIP connection may either operate in a session within which the server remains aware of the image data already cached at the client and it hence doesn't have to transmit again, or it may operate in a stateless mode in which the server has no model of the data already available on the client. In such cases, the client may include a description of its cache model within a proceeding request to avoid retransmission of data already buffered. Unfortunately, the standard defined methods how such cache models are described are very inefficient, and a single request including a cache model may grow several KBytes large for typical images and requests, making the deployment of a JPIP server on top of existing http server infrastructure rather inconvenient. In this work, a lossy and loss less embedded compression scheme for such JPIP cache model adjustment requests based on a modified zero-tree algorithm is proposed, this algorithm works even in constraint environments where request size must remain limited. The proposed algorithm losslessly compresses such cache model adjustment requests often better than by a factor of 1:8, but may even perform a 1:8000 compression in cases where the cache model has to describe a large number of precincts.", "issn" : "1068-0314", "isbn" : "978-1-4673-0715-4", "doi" : "10.1109/DCC.2012.9", "bibtexKey": "richter2012compressing" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cb94055452e475006cdc737c387e30ed/thomasrichter", "tags" : [ "2000;JPEG-XR;codestreams;digital","codec;standard-constraint","codecs;Code","codecs;data","coding","coding;JPEG","coding;Pipelines;Pixel;Standardization;Transform","coding;perceptual","compression","compression;image","image","images;perceivable","loss;perceptual","quality","standards;Codecs;Decoding;Humans;ISO","standards;Image" ], "intraHash" : "cb94055452e475006cdc737c387e30ed", "interHash" : "37fffcc99749f072c9e80b3dbe833a6c", "label" : "Perceptual Image Coding by Standard-Constraint Codecs", "user" : "thomasrichter", "description" : "", "date" : "2016-03-10 09:18:49", "changeDate" : "2016-03-10 08:20:00", "count" : 3, "pub-type": "inproceedings", "booktitle": "Picture Coding Symposium, 2009. PCS 2009","address":"Chicago, IL, USA", "year": "2009", "url": "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5167386", "author": [ "Thomas Richter" ], "authors": [ {"first" : "Thomas", "last" : "Richter"} ], "pages": "1-4","abstract": "A perceptual image compression codec exploits the characteristics of the\nhuman senses to minimize the perceivable quality loss of digital images\nunder compression. Such a codec has an even higher value if the\nresulting codestreams are compatible to an existing standard, and are\nthus decodable by all-day, existing applications. This work will first\ndescribe three basic mechanisms perceptual coding is based on today,\nfollowed by strategies how to implement them in standardized\nenvironments, namely JPEG, JPEG 2000 and JPEG-XR. Following that,\nstrategies to evaluate the success of perceptive coding are discussed,\nnamely subjective measurements and objective quality metrics. Finally,\nthe circle is closed back to compression codecs by showing on the\nexample of JPEG 2000 and SSIM that quality metrics can also be used to\ndrive the rate-allocation of a codec, and hence explore the quality\njudgment of a metric directly.", "doi" : "10.1109/PCS.2009.5167386", "bibtexKey": "richter2009perceptual" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/28d862d4875f17369fa4889bf997da9cc/rainerreichel", "tags" : [ "(mathematics);JPEG","2000","2000;JPIP;Zero-Tree","Coding","JPIP","adjustment","algorithm;Context;Data","browsing","cache","coding;Image","coding;JPEG","coding;protocols;trees","compression","compression;JPIP","compression;image","data","data;interactive","databases;modified","image","information","model","model;embedded","models;Encoding;Image","protocol;medical","request","requests;JPIP","resolution;Servers;Transform","scheme;http","server;cache","state","syntax;image","type","zero-tree" ], "intraHash" : "8d862d4875f17369fa4889bf997da9cc", "interHash" : "a396d444799902c54008c5b834c47262", "label" : "Compressing JPEG 2000 JPIP Cache State Information", "user" : "rainerreichel", "description" : "", "date" : "2016-03-03 17:45:04", "changeDate" : "2016-03-04 09:57:29", "count" : 3, "pub-type": "inproceedings", "booktitle": "Data Compression Conference (DCC), 2012","publisher":"IEEE","address":"Snowbird, Utah, USA", "year": "2012", "url": "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6189232", "author": [ "Thomas Richter" ], "authors": [ {"first" : "Thomas", "last" : "Richter"} ], "editor": [ "James A. Storer Michael W. Marcellin" ], "editors": [ {"first" : "James A. Storer Michael W.", "last" : "Marcellin"} ], "pages": "13-21","abstract": "JPEG 2000 part 9, or short JPIP, is an interactive image browsing protocol that allows the selective delivery of image regions, components or scales from JPEG 2000 image. Typical applications are browsing tools for medical databases where transmitting huge images from server to client in total would be uneconomical. Instead, JPIP allows extracting only the desired image parts for analysis by an http type request syntax. Such a JPIP connection may either operate in a session within which the server remains aware of the image data already cached at the client and it hence doesn't have to transmit again, or it may operate in a stateless mode in which the server has no model of the data already available on the client. In such cases, the client may include a description of its cache model within a proceeding request to avoid retransmission of data already buffered. Unfortunately, the standard defined methods how such cache models are described are very inefficient, and a single request including a cache model may grow several KBytes large for typical images and requests, making the deployment of a JPIP server on top of existing http server infrastructure rather inconvenient. In this work, a lossy and loss less embedded compression scheme for such JPIP cache model adjustment requests based on a modified zero-tree algorithm is proposed, this algorithm works even in constraint environments where request size must remain limited. The proposed algorithm losslessly compresses such cache model adjustment requests often better than by a factor of 1:8, but may even perform a 1:8000 compression in cases where the cache model has to describe a large number of precincts.", "issn" : "1068-0314", "isbn" : "978-1-4673-0715-4", "doi" : "10.1109/DCC.2012.9", "bibtexKey": "Richter2012b" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cb94055452e475006cdc737c387e30ed/rainerreichel", "tags" : [ "2000;JPEG-XR;codestreams;digital","codec;standard-constraint","codecs;Code","codecs;data","coding","coding;JPEG","coding;Pipelines;Pixel;Standardization;Transform","coding;perceptual","compression","compression;image","image","images;perceivable","loss;perceptual","quality","standards;Codecs;Decoding;Humans;ISO","standards;Image" ], "intraHash" : "cb94055452e475006cdc737c387e30ed", "interHash" : "37fffcc99749f072c9e80b3dbe833a6c", "label" : "Perceptual Image Coding by Standard-Constraint Codecs", "user" : "rainerreichel", "description" : "", "date" : "2016-03-03 17:45:04", "changeDate" : "2016-03-04 09:57:29", "count" : 3, "pub-type": "inproceedings", "booktitle": "Picture Coding Symposium, 2009. PCS 2009","address":"Chicago, IL, USA", "year": "2009", "url": "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5167386", "author": [ "Thomas Richter" ], "authors": [ {"first" : "Thomas", "last" : "Richter"} ], "pages": "1-4","abstract": "A perceptual image compression codec exploits the characteristics of the\r\nhuman senses to minimize the perceivable quality loss of digital images\r\nunder compression. Such a codec has an even higher value if the\r\nresulting codestreams are compatible to an existing standard, and are\r\nthus decodable by all-day, existing applications. This work will first\r\ndescribe three basic mechanisms perceptual coding is based on today,\r\nfollowed by strategies how to implement them in standardized\r\nenvironments, namely JPEG, JPEG 2000 and JPEG-XR. Following that,\r\nstrategies to evaluate the success of perceptive coding are discussed,\r\nnamely subjective measurements and objective quality metrics. Finally,\r\nthe circle is closed back to compression codecs by showing on the\r\nexample of JPEG 2000 and SSIM that quality metrics can also be used to\r\ndrive the rate-allocation of a codec, and hence explore the quality\r\njudgment of a metric directly.", "doi" : "10.1109/PCS.2009.5167386", "bibtexKey": "Richter2009c" } ] }