High Quality Compression for Film and Video

Total Page:16

File Type:pdf, Size:1020Kb

High Quality Compression for Film and Video PRESTO – Preservation Technologies for European Broadcast Archives IST-1999-20013 D5.4 – High Quality Compression for Film and Video DOCUMENT IDENTIFIER PRESTO-T54-JRS-20020918 High Quality Compression for Film and Video DATE September 18th, 2002 ABSTRACT This document contains an theoretical as well as practical survey about high quality compression techniques for film and video. AUTHOR, COMPANY P. Schallauer, M. Donoser, G. Kienast, H. Rehatschek (JRS) INTERNAL REVIEWER WORKPACKAGE / TASK T5.4 High Quality Compression DOCUMENT HISTORY Release Date Reason of change Status Distribution 0.1 2002/03/14 Document created Draft Confidential 1.1 2002/04/01 First Content – Compression Draft Confidential 2.1 2002/04/23 Video Codecs – Properties Table Draft Confidential 2.3 2002/05/20 Reviewed Video quality chapter Draft Confidential 3.1 2002/05/26 Pre-Release Version (Theory) Draft Confidential 4.0 2002/06/24 Release Version (Theory) Draft Confidential 5.0 2002/09/18 Practical results included Final Public D5.4 HIGH QUALITY COMPRESSION FOR FILM AND VIDEO PRESTO - IST-1999-20013 09/2002 Table of Contents 1. Scope / Overview.............................................................................................................1 1.1. Scope.............................................................................................................................1 1.2. Overview ........................................................................................................................1 2. Introduction......................................................................................................................3 2.1. What is video compression, and why is it important?....................................................3 2.2. Human Visual System (HVS).........................................................................................6 2.2.1. Physiology of Vision [Nadenau, 2001]....................................................................7 2.2.2. HVS-Model for Imaging Application........................................................................8 2.2.3. Impact of HVS on Image Compression ..................................................................9 2.2.4. Human Visual System – Compression Summary ................................................10 3. Compression Approaches............................................................................................12 3.1. Entropy Coding ............................................................................................................12 3.1.1. Run Length Coding...............................................................................................12 3.1.2. Huffman Coding....................................................................................................12 3.1.3. Arithmetic Coding .................................................................................................13 3.1.4. Lempel/Ziv Coding................................................................................................14 3.1.5. Area Coding..........................................................................................................15 3.2. Intraframe Coding ........................................................................................................15 3.2.1. Predictive Coding..................................................................................................16 3.2.2. Transform Coding.................................................................................................16 3.2.3. Vector Quantization ..............................................................................................19 3.2.4. Subband and Wavelet Coding..............................................................................20 3.2.5. Segmentation and Approximation Coding............................................................21 3.2.6. Fractal Coding ......................................................................................................21 3.2.7. Contour Based Coding .........................................................................................22 3.2.8. Quadtree Compression ........................................................................................22 3.3. Interframe Coding ........................................................................................................23 3.3.1. Frame Differencing ...............................................................................................23 3.3.2. Motion + Motion Compensation............................................................................23 3.3.3. Hybrid Coding .......................................................................................................25 4. Codecs and Formats.....................................................................................................26 4.1. Intraframe Formats ......................................................................................................26 4.1.1. JPEG.....................................................................................................................26 4.1.2. JPEG 2000 ...........................................................................................................27 4.1.3. Tagged Image File Format (TIFF) ........................................................................29 4.1.4. Digital Moving Picture Exchange (DPX)...............................................................31 4.1.5. SGI – RGB............................................................................................................31 4.1.6. PNG – Portable Network Graphics.......................................................................32 4.1.7. DV.........................................................................................................................32 4.1.8. Huffyuv..................................................................................................................33 4.2. Interframe Formats ......................................................................................................34 PRESTO Consortium i D5.4 HIGH QUALITY COMPRESSION FOR FILM AND VIDEO PRESTO - IST-1999-20013 09/2002 4.2.1. Moving Picture Expert Group (MPEG) .................................................................34 4.3. Containers....................................................................................................................39 4.3.1. AVI ........................................................................................................................39 4.3.2. Quicktime..............................................................................................................39 4.3.3. MXF ......................................................................................................................40 4.4. Format Properties Overview ........................................................................................42 5. Standard for Objective Video Quality Measurement .................................................43 5.1. The ANSI Standard for video quality research - ANSI T1.801.03-1996 paper............44 5.1.1. ANSI's Gradient Model of Video Quality...............................................................44 5.1.2. Scalar Features for Video Quality Metrics............................................................45 5.1.3. Feature Comparison.............................................................................................48 5.1.4. ANSI's Video Quality Parameters.........................................................................50 5.1.5. Parameter / Impairment Relationship...................................................................60 6. Quality Measurement ....................................................................................................61 6.1. Measured Parameters .................................................................................................62 6.1.1. PSNR....................................................................................................................62 6.1.2. Negsob and Possob .............................................................................................62 6.2. Measurement Results..................................................................................................64 6.2.1. Lossless Compression on 2k Material..................................................................64 6.2.2. Lossy compression on SDTV material .................................................................67 7. Results and Conclusions..............................................................................................70 7.1. Standards / Formats Overview ....................................................................................70 7.2. Measurements .............................................................................................................71 8. Outlook ...........................................................................................................................73 9. References .....................................................................................................................74 10. Glossary .........................................................................................................................76 PRESTO Consortium ii D5.4 HIGH QUALITY COMPRESSION FOR FILM AND VIDEO PRESTO - IST-1999-20013 09/2002 1. Scope / Overview 1.1. Scope The main purpose of this Deliverable is to evaluate the current possibilities
Recommended publications
  • 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.
    [Show full text]
  • Digital Video Quality Handbook (May 2013
    Digital Video Quality Handbook May 2013 This page intentionally left blank. Executive Summary Under the direction of the Department of Homeland Security (DHS) Science and Technology Directorate (S&T), First Responders Group (FRG), Office for Interoperability and Compatibility (OIC), the Johns Hopkins University Applied Physics Laboratory (JHU/APL), worked with the Security Industry Association (including Steve Surfaro) and members of the Video Quality in Public Safety (VQiPS) Working Group to develop the May 2013 Video Quality Handbook. This document provides voluntary guidance for providing levels of video quality in public safety applications for network video surveillance. Several video surveillance use cases are presented to help illustrate how to relate video component and system performance to the intended application of video surveillance, while meeting the basic requirements of federal, state, tribal and local government authorities. Characteristics of video surveillance equipment are described in terms of how they may influence the design of video surveillance systems. In order for the video surveillance system to meet the needs of the user, the technology provider must consider the following factors that impact video quality: 1) Device categories; 2) Component and system performance level; 3) Verification of intended use; 4) Component and system performance specification; and 5) Best fit and link to use case(s). An appendix is also provided that presents content related to topics not covered in the original document (especially information related to video standards) and to update the material as needed to reflect innovation and changes in the video environment. The emphasis is on the implications of digital video data being exchanged across networks with large numbers of components or participants.
    [Show full text]
  • Video Coding Standards
    Video coding standards Video signals represent sequences of images or frames which can be transmitted with a rate from 15 to 60 frames per second (fps), that provides the illusion of motion in the displayed signal. Unlike images they contain the so-called temporal redundancy. Temporal redundancy arises from repeated objects in consecutive frames of the video sequence. Such objects can remain, they can move horizontally, vertically, or any combination of directions (translation movement), they can fade in and out, and they can disappear from the image as they move out of view. Temporal redundancy Motion compensation A motion compensation technique is used to compensate the temporal redundancy of video sequence. The main idea of the this method is to predict the displacement of group of pixels (usually block of pixels) from their position in the previous frame. Information about this displacement is represented by motion vectors which are transmitted together with the DCT coded difference between the predicted and the original images. Motion compensation Image VLC DCT Quantizer Buffer - Dequantizer Coded DCT coefficients. IDCT Motion compensated predictor Motion Motion vectors estimation Decoded VL image Buffer Dequantizer IDCT decoder Motion compensated predictor Motion vectors Motion compensation Previous frame Current frame Set of macroblocks of the previous frame used to predict the selected macroblock of the current frame Motion compensation Previous frame Current frame Macroblocks of previous frame used to predict current frame Motion compensation Each 16x16 pixel macroblock in the current frame is compared with a set of macroblocks in the previous frame to determine the one that best predicts the current macroblock.
    [Show full text]
  • CHAPTER 10 Basic Video Compression Techniques Contents
    Multimedia Network Lab. CHAPTER 10 Basic Video Compression Techniques Multimedia Network Lab. Prof. Sang-Jo Yoo http://multinet.inha.ac.kr The Graduate School of Information Technology and Telecommunications, INHA University http://multinet.inha.ac.kr Multimedia Network Lab. Contents 10.1 Introduction to Video Compression 10.2 Video Compression with Motion Compensation 10.3 Search for Motion Vectors 10.4 H.261 10.5 H.263 The Graduate School of Information Technology and Telecommunications, INHA University 2 http://multinet.inha.ac.kr Multimedia Network Lab. 10.1 Introduction to Video Compression A video consists of a time-ordered sequence of frames, i.e.,images. Why we need a video compression CIF (352x288) : 35 Mbps HDTV: 1Gbps An obvious solution to video compression would be predictive coding based on previous frames. Compression proceeds by subtracting images: subtract in time order and code the residual error. It can be done even better by searching for just the right parts of the image to subtract from the previous frame. Motion estimation: looking for the right part of motion Motion compensation: shifting pieces of the frame The Graduate School of Information Technology and Telecommunications, INHA University 3 http://multinet.inha.ac.kr Multimedia Network Lab. 10.2 Video Compression with Motion Compensation Consecutive frames in a video are similar – temporal redundancy Frame rate of the video is relatively high (30 frames/sec) Camera parameters (position, angle) usually do not change rapidly. Temporal redundancy is exploited so that not every frame of the video needs to be coded independently as a new image. The difference between the current frame and other frame(s) in the sequence will be coded – small values and low entropy, good for compression.
    [Show full text]
  • (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.
    [Show full text]
  • On the Accuracy of Video Quality Measurement Techniques
    On the accuracy of video quality measurement techniques Deepthi Nandakumar Yongjun Wu Hai Wei Avisar Ten-Ami Amazon Video, Bangalore, India Amazon Video, Seattle, USA Amazon Video, Seattle, USA Amazon Video, Seattle, USA, [email protected] [email protected] [email protected] [email protected] Abstract —With the massive growth of Internet video and therefore measuring and optimizing for video quality in streaming, it is critical to accurately measure video quality these high quality ranges is an imperative for streaming service subjectively and objectively, especially HD and UHD video which providers. In this study, we lay specific emphasis on the is bandwidth intensive. We summarize the creation of a database measurement accuracy of subjective and objective video quality of 200 clips, with 20 unique sources tested across a variety of scores in this high quality range. devices. By classifying the test videos into 2 distinct quality regions SD and HD, we show that the high correlation claimed by objective Globally, a healthy mix of devices with different screen sizes video quality metrics is led mostly by videos in the SD quality and form factors is projected to contribute to IP traffic in 2021 region. We perform detailed correlation analysis and statistical [1], ranging from smartphones (44%), to tablets (6%), PCs hypothesis testing of the HD subjective quality scores, and (19%) and TVs (24%). It is therefore necessary for streaming establish that the commonly used ACR methodology of subjective service providers to quantify the viewing experience based on testing is unable to capture significant quality differences, leading the device, and possibly optimize the encoding and delivery to poor measurement accuracy for both subjective and objective process accordingly.
    [Show full text]
  • 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
    [Show full text]
  • Overview: Motion-Compensated Coding
    Overview: motion-compensated coding Motion-compensated prediction Motion-compensated hybrid coding Motion estimation by block-matching Motion estimation with sub-pixel accuracy Power spectral density of the motion-compensated prediction error Rate-distortion analysis Loop filter Motion compensated coding with sub-pixel accuracy Rate-constrained motion estimation Bernd Girod: EE398B Image Communication II Motion Compensated Coding no. 1 Motion-compensated prediction previous frame stationary Δt background current frame x time t y moving object ⎛ d x ⎞ „Displacement vector“ ⎜ d ⎟ shifted ⎝ y ⎠ object Prediction for the luminance signal S(x,y,t) within the moving object: ˆ S (x, y,t) = S(x − dx , y − dy ,t − Δt) Bernd Girod: EE398B Image Communication II Motion Compensated Coding no. 2 Combining transform coding and prediction Transform domain prediction Space domain prediction Q Q T - T - T T −1 T −1 T −1 PT T PTS T T −1 T−1 PT PS Bernd Girod: EE398B Image Communication II Motion Compensated Coding no. 3 Motion-compensated hybrid coder Coder Control Control Data Intra-frame DCT DCT Coder - Coefficients Decoder Intra-frame Decoder 0 Motion- Compensated Intra/Inter Predictor Motion Data Motion Estimator Bernd Girod: EE398B Image Communication II Motion Compensated Coding no. 4 Motion-compensated hybrid decoder Control Data DCT Coefficients Decoder Intra-frame Decoder 0 Motion- Compensated Intra/Inter Predictor Motion Data Bernd Girod: EE398B Image Communication II Motion Compensated Coding no. 5 Block-matching algorithm search range in Subdivide current reference frame frame into blocks. Sk −1 Find one displacement vector for each block. Within a search range, find a best „match“ that minimizes an error measure.
    [Show full text]
  • Efficient Video Coding with Motion-Compensated Orthogonal
    Efficient Video Coding with Motion-Compensated Orthogonal Transforms DU LIU Master’s Degree Project Stockholm, Sweden 2011 XR-EE-SIP 2011:011 Efficient Video Coding with Motion-Compensated Orthogonal Transforms Du Liu July, 2011 Abstract Well-known standard hybrid coding techniques utilize the concept of motion- compensated predictive coding in a closed-loop. The resulting coding de- pendencies are a major challenge for packet-based networks like the Internet. On the other hand, subband coding techniques avoid the dependencies of predictive coding and are able to generate video streams that better match packet-based networks. An interesting class for subband coding is the so- called motion-compensated orthogonal transform. It generates orthogonal subband coefficients for arbitrary underlying motion fields. In this project, a theoretical lossless signal model based on Gaussian distribution is proposed. It is possible to obtain the optimal rate allocation from this model. Addition- ally, a rate-distortion efficient video coding scheme is developed that takes advantage of motion-compensated orthogonal transforms. The scheme com- bines multiple types of motion-compensated orthogonal transforms, variable block size, and half-pel accurate motion compensation. The experimental results show that this scheme outperforms individual motion-compensated orthogonal transforms. i Acknowledgements This thesis was carried out at Sound and Image Processing Lab, School of Electrical Engineering, KTH. I would like to express my appreciation to my supervisor Markus Flierl for the opportunity of doing this thesis. I am grateful for his patience and valuable suggestions and discussions. Many thanks to Haopeng Li, Mingyue Li, and Zhanyu Ma, who helped me a lot during my research.
    [Show full text]
  • The H.264 Advanced Video Coding (AVC) Standard
    Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered.
    [Show full text]
  • Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: a Simulation and a Phantom Study
    Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2013, Article ID 520540, 10 pages http://dx.doi.org/10.1155/2013/520540 Research Article Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study Marco Bögel,1 Hannes G. Hofmann,1 Joachim Hornegger,1 Rebecca Fahrig,2 Stefan Britzen,3 and Andreas Maier3 1 Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany 2 Department of Radiology, Lucas MRS Center, Stanford University, Palo Alto, CA 94304, USA 3 Siemens AG, Healthcare Sector, 91301 Forchheim, Germany Correspondence should be addressed to Andreas Maier; [email protected] Received 21 February 2013; Revised 13 May 2013; Accepted 15 May 2013 Academic Editor: Michael W. Vannier Copyright © 2013 Marco Bogel¨ et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Long acquisition times lead to image artifacts in thoracic C-arm CT. Motion blur caused by respiratory motion leads to decreased image quality in many clinical applications. We introduce an image-based method to estimate and compensate respiratory motion in C-arm CT based on diaphragm motion. In order to estimate respiratory motion, we track the contour of the diaphragm in the projection image sequence. Using a motion corrected triangulation approach on the diaphragm vertex, we are able to estimate a motion signal. The estimated motion signal is used to compensate for respiratory motion in the target region, for example, heart or lungs.
    [Show full text]
  • FFV1 Video Codec Specification
    FFV1 Video Codec Specification by Michael Niedermayer [email protected] Contents 1 Introduction 2 2 Terms and Definitions 2 2.1 Terms ................................................. 2 2.2 Definitions ............................................... 2 3 Conventions 3 3.1 Arithmetic operators ......................................... 3 3.2 Assignment operators ........................................ 3 3.3 Comparison operators ........................................ 3 3.4 Order of operation precedence .................................... 4 3.5 Range ................................................. 4 3.6 Bitstream functions .......................................... 4 4 General Description 5 4.1 Border ................................................. 5 4.2 Median predictor ........................................... 5 4.3 Context ................................................ 5 4.4 Quantization ............................................. 5 4.5 Colorspace ............................................... 6 4.5.1 JPEG2000-RCT ....................................... 6 4.6 Coding of the sample difference ................................... 6 4.6.1 Range coding mode ..................................... 6 4.6.2 Huffman coding mode .................................... 9 5 Bitstream 10 5.1 Configuration Record ......................................... 10 5.1.1 In AVI File Format ...................................... 11 5.1.2 In ISO/IEC 14496-12 (MP4 File Format) ......................... 11 5.1.3 In NUT File Format ....................................
    [Show full text]