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Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression
Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression Rahul Vanam A dissertation submitted in partial ful¯llment of the requirements for the degree of Doctor of Philosophy University of Washington 2010 Program Authorized to O®er Degree: Electrical Engineering University of Washington Graduate School This is to certify that I have examined this copy of a doctoral dissertation by Rahul Vanam and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the ¯nal examining committee have been made. Co-Chairs of the Supervisory Committee: Eve A. Riskin Richard E. Ladner Reading Committee: Eve A. Riskin Richard E. Ladner Maya R. Gupta Date: In presenting this dissertation in partial ful¯llment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this dissertation is allowable only for scholarly purposes, consistent with \fair use" as prescribed in the U.S. Copyright Law. Requests for copying or reproduction of this dissertation may be referred to Proquest Information and Learning, 300 North Zeeb Road, Ann Arbor, MI 48106-1346, 1-800-521-0600, to whom the author has granted \the right to reproduce and sell (a) copies of the manuscript in microform and/or (b) printed copies of the manuscript made from microform." Signature Date University of Washington Abstract Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression Rahul Vanam Co-Chairs of the Supervisory Committee: Professor Eve A. -
Optimization Methods for Data Compression
OPTIMIZATION METHODS FOR DATA COMPRESSION A Dissertation Presented to The Faculty of the Graduate School of Arts and Sciences Brandeis University Computer Science James A. Storer Advisor In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy by Giovanni Motta May 2002 This dissertation, directed and approved by Giovanni Motta's Committee, has been accepted and approved by the Graduate Faculty of Brandeis University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY _________________________ Dean of Arts and Sciences Dissertation Committee __________________________ James A. Storer ____________________________ Martin Cohn ____________________________ Jordan Pollack ____________________________ Bruno Carpentieri ii To My Parents. iii ACKNOWLEDGEMENTS I wish to thank: Bruno Carpentieri, Martin Cohn, Antonella Di Lillo, Jordan Pollack, Francesco Rizzo, James Storer for their support and collaboration. I also thank Jeanne DeBaie, Myrna Fox, Julio Santana for making my life at Brandeis easier and enjoyable. iv ABSTRACT Optimization Methods for Data Compression A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Giovanni Motta Many data compression algorithms use ad–hoc techniques to compress data efficiently. Only in very few cases, can data compressors be proved to achieve optimality on a specific information source, and even in these cases, algorithms often use sub–optimal procedures in their execution. It is appropriate to ask whether the replacement of a sub–optimal strategy by an optimal one in the execution of a given algorithm results in a substantial improvement of its performance. Because of the differences between algorithms the answer to this question is domain dependent and our investigation is based on a case–by–case analysis of the effects of using an optimization procedure in a data compression algorithm. -
Data Compression II (Codecs and Container Formats)
Data Compression II (Codecs and Container Formats) 7. Data Compression II - Copyright © Denis Hamelin - Ryerson University Codecs A codec is a device or program capable of performing encoding and decoding on a digital data stream or signal. The word codec may be a combination of any of the following: 'compressor-decompressor', 'coder-decoder', or 'compression/decompression algorithm'. 7. Data Compression II - Copyright © Denis Hamelin - Ryerson University Codecs (Usage) Codecs encode a stream or signal for transmission, storage or encryption and decode it for viewing or editing. Codecs are often used in videoconferencing and streaming media applications. An audio compressor converts analogue audio signals into digital signals for transmission or storage. A receiving device then converts the digital signals back to analogue using an audio decompressor, for playback. 7. Data Compression II - Copyright © Denis Hamelin - Ryerson University Codecs Most codecs are lossy, allowing the compressed data to be made smaller in size. There are also lossless codecs, but for most purposes the slight increase in quality might not be worth the increase in data size, which is often considerable. Codecs are often designed to emphasise certain aspects of the media to be encoded (motion vs. color for example). 7. Data Compression II - Copyright © Denis Hamelin - Ryerson University Codec Compatibility There are hundreds or even thousands of codecs ranging from those downloadable for free to ones costing hundreds of dollars or more. This can create compatibility and obsolescence issues. By contrast, lossless PCM audio (44.1 kHz, 16 bit stereo, as represented on an audio CD or in a .wav or .aiff file) offers more of a persistent standard across multiple platforms and over time. -
University of São Paulo
UNIVERSITY OF SÃO PAULO Institute of Mathematical Sciences and Computing Screencast recording Marco Aurélio Graciotto Silva Ellen Francine Barbosa José Carlos Maldonado n. XXX TECHNICAL REPORT São Carlos - SP Novembro/2011 UNIVERSITY OF SÃO PAULO Institute of Mathematical Sciences and Computing ISSN: 0103-2569 Screencast recording Marco Aurélio Graciotto Silva Ellen Francine Barbosa José Carlos Maldonado n. XXX TECHNICAL REPORT São Carlos - SP Novembro/2011 Contents 1 Introduction 1 1.1 Text organization . .2 1.2 Text conventions . .3 2 Planning 5 2.1 Script creation . .5 2.2 Script testing . .6 3 Screencast recording 9 3.1 Requirements and baseline configuration . .9 3.2 Recording . 12 3.2.1 Wink . 13 3.2.2 Xvidcap . 13 3.2.3 Jing . 14 3.2.4 CamStudio . 14 3.3 Concluding remarks . 14 4 Screencast edition 17 4.1 Filters . 17 5 Screencast encoding 19 5.1 Video encoding . 19 5.2 Audio encoding . 22 5.3 Snapshots creation . 22 6 Release 25 v vi Chapter1 Introduction A screencast is a movie-record of a running application (GANNOD; BURGE; HELMICK, 2008). Its main application is to demonstrate a specific functionality of a tool or just to showcase its main features. In the educational setting, screencasts are used to deliver lectures, usually through a subscription-based broadcast- ing of video (podcasts), meant to be watched before commencing the class (LAGE; PLATT; TREGLIA, 2001) or after it, to reinforce the learning activity. An approach that uses screencasts before the class is the inverted classroom (LAGE; PLATT; TREGLIA, 2001). It consists of a teaching environment that mixes use of technology with hands-on activities: in- class lecture time is replaced with laboratory and in-class activities; outside-class time is used to watch lectures (GANNOD; BURGE; HELMICK, 2008). -
1 SUBBAND IMAGE COMPRESSION Aria Nosratinia1, Geoffrey Davis2, Zixiang Xiong3, and Rajesh Rajagopalan4
1 SUBBAND IMAGE COMPRESSION Aria Nosratinia1, Geo®rey Davis2, Zixiang Xiong3, and Rajesh Rajagopalan4 1Dept of Electrical and Computer Engineering, Rice University, Houston, TX 77005 2Math Department, Dartmouth College, Hanover, NH 03755 3Dept. of Electrical Engineering, University of Hawaii, Honolulu, HI 96822 4Lucent Technologies, Murray Hill, NJ 07974 Abstract: This chapter presents an overview of subband/wavelet image com- pression. Shannon showed that optimality in compression can only be achieved with Vector Quantizers (VQ). There are practical di±culties associated with VQ, however, that motivate transform coding. In particular, reverse water¯ll- ing arguments motivate subband coding. Using a simpli¯ed model of a subband coder, we explore key design issues. The role of smoothness and compact sup- port of the basis elements in compression performance are addressed. We then look at the evolution of practical subband image coders. We present the rudi- ments of three generations of subband coders, which have introduced increasing degrees of sophistication and performance in image compression: The ¯rst gen- eration attracted much attention and interest by introducing the zerotree con- cept. The second generation used adaptive space-frequency and rate-distortion optimized techniques. These ¯rst two generations focused largely on inter-band dependencies. The third generation includes exploitation of intra-band depen- dencies, utilizing trellis-coded quantization and estimation-based methods. We conclude by a summary and discussion of future trends in subband image com- pression. 1 2 1.1 INTRODUCTION Digital imaging has had an enormous impact on industrial applications and scienti¯c projects. It is no surprise that image coding has been a subject of great commercial interest.