H.261 Implementation on the TMS320C80 DSP
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												On Computational Complexity of Motion Estimation Algorithms in MPEG-4 Encoder
On Computational Complexity of Motion Estimation Algorithms in MPEG-4 Encoder Muhammad Shahid This thesis report is presented as a part of degree of Master of Science in Electrical Engineering Blekinge Institute of Technology, 2010 Supervisor: Tech Lic. Andreas Rossholm, ST-Ericsson Examiner: Dr. Benny Lovstrom, Blekinge Institute of Technology Abstract Video Encoding in mobile equipments is a computationally demanding fea- ture that requires a well designed and well developed algorithm. The op- timal solution requires a trade off in the encoding process, e.g. motion estimation with tradeoff between low complexity versus high perceptual quality and efficiency. The present thesis works on reducing the complexity of motion estimation algorithms used for MPEG-4 video encoding taking SLIMPEG motion estimation algorithm as reference. The inherent prop- erties of video like spatial and temporal correlation have been exploited to test new techniques of motion estimation. Four motion estimation algo- rithms have been proposed. The computational complexity and encoding quality have been evaluated. The resulting encoded video quality has been compared against the standard Full Search algorithm. At the same time, reduction in computational complexity of the improved algorithm is com- pared against SLIMPEG which is already about 99 % more efficient than Full Search in terms of computational complexity. The fourth proposed algorithm, Adaptive SAD Control, offers a mechanism of choosing trade off between computational complexity and encoding quality in a dynamic way. Acknowledgements It is a matter of great pleasure to express my deepest gratitude to my ad- visors Dr. Benny L¨ovstr¨om and Andreas Rossholm for all their guidance, support and encouragement throughout my thesis work. - 
												
												Kulkarni Uta 2502M 11649.Pdf
IMPLEMENTATION OF A FAST INTER-PREDICTION MODE DECISION IN H.264/AVC VIDEO ENCODER by AMRUTA KIRAN KULKARNI Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING THE UNIVERSITY OF TEXAS AT ARLINGTON May 2012 ACKNOWLEDGEMENTS First and foremost, I would like to take this opportunity to offer my gratitude to my supervisor, Dr. K.R. Rao, who invested his precious time in me and has been a constant support throughout my thesis with his patience and profound knowledge. His motivation and enthusiasm helped me in all the time of research and writing of this thesis. His advising and mentoring have helped me complete my thesis. Besides my advisor, I would like to thank the rest of my thesis committee. I am also very grateful to Dr. Dongil Han for his continuous technical advice and financial support. I would like to acknowledge my research group partner, Santosh Kumar Muniyappa, for all the valuable discussions that we had together. It helped me in building confidence and motivated towards completing the thesis. Also, I thank all other lab mates and friends who helped me get through two years of graduate school. Finally, my sincere gratitude and love go to my family. They have been my role model and have always showed me right way. Last but not the least; I would like to thank my husband Amey Mahajan for his emotional and moral support. April 20, 2012 ii ABSTRACT IMPLEMENTATION OF A FAST INTER-PREDICTION MODE DECISION IN H.264/AVC VIDEO ENCODER Amruta Kulkarni, M.S The University of Texas at Arlington, 2011 Supervising Professor: K.R. - 
												
												Music 422 Project Report
Music 422 Project Report Jatin Chowdhury, Arda Sahiner, and Abhipray Sahoo October 10, 2020 1 Introduction We present an audio coder that seeks to improve upon traditional coding methods by implementing block switching, spectral band replication, and gain-shape quantization. Block switching improves on coding of transient sounds. Spectral band replication exploits low sensitivity of human hearing towards high frequencies. It fills in any missing coded high frequency content with information from the low frequencies. We chose to do gain-shape quantization in order to explicitly code the energy of each band, preserving the perceptually important spectral envelope more accurately. 2 Implementation 2.1 Block Switching Block switching, as introduced by Edler [1], allows the audio coder to adaptively change the size of the block being coded. Modern audio coders typically encode frequency lines obtained using the Modified Discrete Cosine Transform (MDCT). Using longer blocks for the MDCT allows forbet- ter spectral resolution, while shorter blocks have better time resolution, due to the time-frequency trade-off known as the Fourier uncertainty principle [2]. Due to their poor time resolution, using long MDCT blocks for an audio coder can result in “pre-echo” when the coder encodes transient sounds. Block switching allows the coder to use long MDCT blocks for normal signals being encoded and use shorter blocks for transient parts of the signal. In our coder, we implement the block switching algorithm introduced in [1]. This algorithm consists of four types of block windows: long, short, start, and stop. Long and short block windows are simple sine windows, as defined in [3]: (( ) ) 1 π w[n] = sin n + (1) 2 N where N is the length of the block. - 
												
												Video Codec Requirements and Evaluation Methodology
Video Codec Requirements 47pt 30pt and Evaluation Methodology Color::white : LT Medium Font to be used by customers and : Arial www.huawei.com draft-filippov-netvc-requirements-01 Alexey Filippov, Huawei Technologies 35pt Contents Font to be used by customers and partners : • An overview of applications • Requirements 18pt • Evaluation methodology Font to be used by customers • Conclusions and partners : Slide 2 Page 2 35pt Applications Font to be used by customers and partners : • Internet Protocol Television (IPTV) • Video conferencing 18pt • Video sharing Font to be used by customers • Screencasting and partners : • Game streaming • Video monitoring / surveillance Slide 3 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : • Basic requirements: . Random access to pictures 18pt Random Access Period (RAP) should be kept small enough (approximately, 1-15 seconds); Font to be used by customers . Temporal (frame-rate) scalability; and partners : . Error robustness • Optional requirements: . resolution and quality (SNR) scalability Slide 4 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : Resolution Frame-rate, fps Picture access mode 2160p (4K),3840x2160 60 RA 18pt 1080p, 1920x1080 24, 50, 60 RA 1080i, 1920x1080 30 (60 fields per second) RA Font to be used by customers and partners : 720p, 1280x720 50, 60 RA 576p (EDTV), 720x576 25, 50 RA 576i (SDTV), 720x576 25, 30 RA 480p (EDTV), 720x480 50, 60 RA 480i (SDTV), 720x480 25, 30 RA Slide 5 35pt Video conferencing Font to be used by customers and partners : • Basic requirements: . Delay should be kept as low as possible 18pt The preferable and maximum delay values should be less than 100 ms and 350 ms, respectively Font to be used by customers . - 
												
												1. in the New Document Dialog Box, on the General Tab, for Type, Choose Flash Document, Then Click______
1. In the New Document dialog box, on the General tab, for type, choose Flash Document, then click____________. 1. Tab 2. Ok 3. Delete 4. Save 2. Specify the export ________ for classes in the movie. 1. Frame 2. Class 3. Loading 4. Main 3. To help manage the files in a large application, flash MX professional 2004 supports the concept of _________. 1. Files 2. Projects 3. Flash 4. Player 4. In the AppName directory, create a subdirectory named_______. 1. Source 2. Flash documents 3. Source code 4. Classes 5. In Flash, most applications are _________ and include __________ user interfaces. 1. Visual, Graphical 2. Visual, Flash 3. Graphical, Flash 4. Visual, AppName 6. Test locally by opening ________ in your web browser. 1. AppName.fla 2. AppName.html 3. AppName.swf 4. AppName 7. The AppName directory will contain everything in our project, including _________ and _____________ . 1. Source code, Final input 2. Input, Output 3. Source code, Final output 4. Source code, everything 8. In the AppName directory, create a subdirectory named_______. 1. Compiled application 2. Deploy 3. Final output 4. Source code 9. Every Flash application must include at least one ______________. 1. Flash document 2. AppName 3. Deploy 4. Source 10. In the AppName/Source directory, create a subdirectory named __________. 1. Source 2. Com 3. Some domain 4. AppName 11. In the AppName/Source/Com directory, create a sub directory named ______ 1. Some domain 2. Com 3. AppName 4. Source 12. A project is group of related _________ that can be managed via the project panel in the flash. - 
												
												An Overview of Block Matching Algorithms for Motion Vector Estimation
Proceedings of the Second International Conference on Research in DOI: 10.15439/2017R85 Intelligent and Computing in Engineering pp. 217–222 ACSIS, Vol. 10 ISSN 2300-5963 An Overview of Block Matching Algorithms for Motion Vector Estimation Sonam T. Khawase1, Shailesh D. Kamble2, Nileshsingh V. Thakur3, Akshay S. Patharkar4 1PG Scholar, Computer Science & Engineering, Yeshwantrao Chavan College of Engineering, India 2Computer Science & Engineering, Yeshwantrao Chavan College of Engineering, India 3Computer Science & Engineering, Prof Ram Meghe College of Engineering & Management, India 4Computer Technology, K.D.K. College of Engineering, India [email protected], [email protected],[email protected], [email protected] Abstract–In video compression technique, motion estimation is one of the key components because of its high computation complexity involves in finding the motion vectors (MV) between the frames. The purpose of motion estimation is to reduce the storage space, bandwidth and transmission cost for transmission of video in many multimedia service applications by reducing the temporal redundancies while maintaining a good quality of the video. There are many motion estimation algorithms, but there is a trade-off between algorithms accuracy and speed. Among all of these, block-based motion estimation algorithms are most robust and versatile. In motion estimation, a variety of fast block based matching algorithms has been proposed to address the issues such as reducing the number of search/checkpoints, computational cost, and complexities etc. Due to its simplicity, the Fig. 1. Classification of Frames block-based technique is most popular. Motion estimation is only known for video coding process but for solving real life redundancies. - 
												
												Discrete Cosine Transform for 8X8 Blocks with CUDA
Discrete Cosine Transform for 8x8 Blocks with CUDA Anton Obukhov [email protected] Alexander Kharlamov [email protected] October 2008 Document Change History Version Date Responsible Reason for Change 0.8 24.03.2008 Alexander Kharlamov Initial release 0.9 25.03.2008 Anton Obukhov Added algorithm-specific parts, fixed some issues 1.0 17.10.2008 Anton Obukhov Revised document structure October 2008 2 Abstract In this whitepaper the Discrete Cosine Transform (DCT) is discussed. The two-dimensional variation of the transform that operates on 8x8 blocks (DCT8x8) is widely used in image and video coding because it exhibits high signal decorrelation rates and can be easily implemented on the majority of contemporary computing architectures. The key feature of the DCT8x8 is that any pair of 8x8 blocks can be processed independently. This makes possible fully parallel implementation of DCT8x8 by definition. Most of CPU-based implementations of DCT8x8 are firmly adjusted for operating using fixed point arithmetic but still appear to be rather costly as soon as blocks are processed in the sequential order by the single ALU. Performing DCT8x8 computation on GPU using NVIDIA CUDA technology gives significant performance boost even compared to a modern CPU. The proposed approach is accompanied with the sample code “DCT8x8” in the NVIDIA CUDA SDK. October 2008 3 1. Introduction The Discrete Cosine Transform (DCT) is a Fourier-like transform, which was first proposed by Ahmed et al . (1974). While the Fourier Transform represents a signal as the mixture of sines and cosines, the Cosine Transform performs only the cosine-series expansion. - 
												
												Motion Estimation at the Decoder Sven Klomp and Jorn¨ Ostermann Leibniz Universit¨At Hannover Germany
5 Motion Estimation at the Decoder Sven Klomp and Jorn¨ Ostermann Leibniz Universit¨at Hannover Germany 1. Introduction All existing video coding standards, such as MPEG-1,2,4 or ITU-T H.26x, perform motion estimation at the encoder in order to exploit temporal dependencies within the video sequence. The estimated motion vectors are transmitted and used by the decoder to assemble a prediction of the current frame. Since only the prediction error and the motion information are transmitted, instead of intra coding the pixel values, compression is achieved. Due to block-based motion estimation, accurate compensation at object borders can only be achieved with small block sizes. Large blocks may contain several objects which move in different directions. Thus, accurate motion estimation and compensation is not possible, as shown by Klomp et al. (2010a) using prediction error variance, and small block sizes are favourable. However, the smaller the block, the more motion vectors have to be transmitted, resulting in a contradiction to bit rate reduction. 4.0 3.5 3.0 2.5 MV Backward MV Forward MV Bidirectional 2.0 RS Backward Rate (bit/pixel) RS Forward 1.5 RS Bidirectional 1.0 0.5 0.0 2 4 8 16 32 Block Size Fig. 1. Data rates of residual (RS) and motion vectors (MV) for different motion compensation techniques (Kimono sequence). 782 Effective Video Coding for MultimediaVideo Applications Coding These characteristics can be observed in Figure 1, where the rates for the residual and the motion vectors are plotted for different block sizes and three prediction techniques. - 
												
												(A/V Codecs) REDCODE RAW (.R3D) ARRIRAW
What is a Codec? Codec is a portmanteau of either "Compressor-Decompressor" or "Coder-Decoder," which describes a device or program capable of performing transformations on a data stream or signal. 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 solutions. A video codec converts analog video signals from a video camera into digital signals for transmission. It then converts the digital signals back to analog for display. An audio codec converts analog audio signals from a microphone into digital signals for transmission. It then converts the digital signals back to analog for playing. The raw encoded form of audio and video data is often called essence, to distinguish it from the metadata information that together make up the information content of the stream and any "wrapper" data that is then added to aid access to or improve the robustness of the stream. Most codecs are lossy, in order to get a reasonably small file size. There are lossless codecs as well, but for most purposes the almost imperceptible increase in quality is not worth the considerable increase in data size. The main exception is if the data will undergo more processing in the future, in which case the repeated lossy encoding would damage the eventual quality too much. Many multimedia data streams need to contain both audio and video data, and often some form of metadata that permits synchronization of the audio and video. Each of these three streams may be handled by different programs, processes, or hardware; but for the multimedia data stream to be useful in stored or transmitted form, they must be encapsulated together in a container format. - 
												
												Analysis Application for H.264 Video Encoding
IT 10 061 Examensarbete 30 hp November 2010 Analysis Application for H.264 Video Encoding Ying Wang Institutionen för informationsteknologi Department of Information Technology Abstract Analysis Application for H.264 Video Encoding Ying Wang Teknisk- naturvetenskaplig fakultet UTH-enheten A video analysis application ERANA264(Ericsson Research h.264 video Besöksadress: ANalysis Application) is developed in Ångströmlaboratoriet Lägerhyddsvägen 1 this project. Erana264 is a tool that Hus 4, Plan 0 analyzes H.264 encoded video bitstreams, extracts the encoding information and Postadress: parameters, analyzes them in different Box 536 751 21 Uppsala stages and displays the results in a user friendly way. The intention is that Telefon: such an application would be used during 018 – 471 30 03 development and testing of video codecs. Telefax: The work is implemented on top of 018 – 471 30 00 existing H.264 encoder/decoder source code (C/C++) developed at Ericsson Hemsida: Research. http://www.teknat.uu.se/student Erana264 consists of three layers. The first layer is the H.264 decoder previously developed in Ericsson Research. By using the decoder APIs, the information is extracted from the bitstream and is sent to the higher layers. The second layer visualizes the different decoding stages, uses overlay to display some macro block and picture level information and provides a set of play back functions. The third layer analyzes and presents the statistics of prominent parameters in video compression process, such as video quality measurements, motion vector distribution, picture bit distribution etc. Key words: H.264, Video compression, Bitstream analysis, Video encoding Handledare: Zhuangfei Wu and Clinton Priddle Ämnesgranskare: Cris Luengo Examinator: Anders Jansson IT10061 Tryckt av: Reprocentralen ITC Acknowledgements Fist of all, I am heartily thankful to my supervisors, Fred Wu and Clinton Priddle, whose encouragement, supervision and support from the preliminary to the concluding level enabled me to develop an understanding of the subject. - 
												
												Multiple Reference Motion Compensation: a Tutorial Introduction and Survey Contents
Foundations and TrendsR in Signal Processing Vol. 2, No. 4 (2008) 247–364 c 2009 A. Leontaris, P. C. Cosman and A. M. Tourapis DOI: 10.1561/2000000019 Multiple Reference Motion Compensation: A Tutorial Introduction and Survey By Athanasios Leontaris, Pamela C. Cosman and Alexis M. Tourapis Contents 1 Introduction 248 1.1 Motion-Compensated Prediction 249 1.2 Outline 254 2 Background, Mosaic, and Library Coding 256 2.1 Background Updating and Replenishment 257 2.2 Mosaics Generated Through Global Motion Models 261 2.3 Composite Memories 264 3 Multiple Reference Frame Motion Compensation 268 3.1 A Brief Historical Perspective 268 3.2 Advantages of Multiple Reference Frames 270 3.3 Multiple Reference Frame Prediction 271 3.4 Multiple Reference Frames in Standards 277 3.5 Interpolation for Motion Compensated Prediction 281 3.6 Weighted Prediction and Multiple References 284 3.7 Scalable and Multiple-View Coding 286 4 Multihypothesis Motion-Compensated Prediction 290 4.1 Bi-Directional Prediction and Generalized Bi-Prediction 291 4.2 Overlapped Block Motion Compensation 294 4.3 Hypothesis Selection Optimization 296 4.4 Multihypothesis Prediction in the Frequency Domain 298 4.5 Theoretical Insight 298 5 Fast Multiple-Frame Motion Estimation Algorithms 301 5.1 Multiresolution and Hierarchical Search 302 5.2 Fast Search using Mathematical Inequalities 303 5.3 Motion Information Re-Use and Motion Composition 304 5.4 Simplex and Constrained Minimization 306 5.5 Zonal and Center-biased Algorithms 307 5.6 Fractional-pixel Texture Shifts or Aliasing - 
												
												CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter Prediction using Binary classification techniques A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering by Alex Kit Romero May 2020 The graduate project of Alex Kit Romero is approved: ____________________________________ ____________ Dr. Katya Mkrtchyan Date ____________________________________ ____________ Dr. Kyle Dewey Date ____________________________________ ____________ Dr. John J. Noga, Chair Date California State University, Northridge ii Dedication This project is dedicated to all of the Computer Science professors that I have come in contact with other the years who have inspired and encouraged me to pursue a career in computer science. The words and wisdom of these professors are what pushed me to try harder and accomplish more than I ever thought possible. I would like to give a big thanks to the open source community and my fellow cohort of computer science co-workers for always being there with answers to my numerous questions and inquiries. Without their guidance and expertise, I could not have been successful. Lastly, I would like to thank my friends and family who have supported and uplifted me throughout the years. Thank you for believing in me and always telling me to never give up. iii Table of Contents Signature Page ................................................................................................................................ ii Dedication .....................................................................................................................................