DOCSLIB.ORG

On Computational Complexity of Motion Estimation Algorithms in MPEG-4 Encoder

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. It was nonethe- less a great opportunity to do research work at ST-Ericsson under the marvelous supervision of Andreas Rossholm. The counseling provided by Benny L¨ovstr¨om was of great value for me in writing up this manuscript. I can’t forget mentioning the comfort I received from Fredrik Nillson and Jimmy Rubin of ST-E in setting up the working environment and start up of ST-E algorithm. I owe my successes in life so far to all of my family members, for their magnificent kindness and love! iii Contents Abstract i Acknowledgements iii Contents v 1 Introduction 1 2 Basics of Digital Video 3 2.1ColorSpaces.......................... 3 2.2VideoQuality.......................... 4 2.3RepresentationofDigitalVideo................ 4 2.4Applications........................... 4 2.4.1 Internet......................... 5 2.4.2 VideoStorage...................... 5 2.4.3 Television........................ 5 2.4.4 GamesandEntertainment............... 6 2.4.5 VideoTelephony.................... 6 3 Video Compression Fundamentals 7 3.1CODEC............................. 7 3.2AVideoCODEC........................ 8 3.3VideoCodingStandards.................... 9 3.3.1 MPEG-1........................ 10 3.3.2 MPEG-2........................ 10 3.3.3 MPEG-4........................ 10 3.3.4 MPEG-7........................ 10 3.3.5 MPEG-21........................ 10 3.3.6 H.261.......................... 11 3.3.7 H.263.......................... 11 3.3.8 H.263+......................... 11 3.3.9 H.264.......................... 11 3.4MPEG-4............................. 11 v Contents 3.5Syntax.............................. 12 4 Motion Estimation and its Implementation 15 4.1BlockMatching......................... 17 4.2MotionEstimationAlgorithms................ 18 4.2.1 FullSearch....................... 19 4.2.2 Three-StepSearch................... 20 4.2.3 DiamondSearch.................... 20 4.2.4 SLIMPEG........................ 21 5 Rate Distortion Optimization and Bjontegaard Delta PSNR 23 5.1MeasurementofDistortion.................. 24 5.2 Bjontegaard Delta PSNR . 24 6 Simulation, Results and Discussion 29 6.1SADasaComparisonMetric................. 30 6.2ProposedTechniques...................... 30 6.2.1 Spatial Correlation Algorithm . 31 6.2.2 Temporal Correlation Algorithm . 31 6.2.3 AdaptiveSADControl................. 32 6.3 Simulations with different video sequences . 34 6.3.1 FootballSequence................... 35 6.3.2 ForemanSequence................... 36 6.3.3 ClaireSequence..................... 40 7 Conclusion and Future Work 51 List of figures 54 List of tables 55 Bibliography 57 vi Chapter 1 Introduction Since the advent of the first digital video coding technology standard in 1984 by the International Telecommunication Union (ITU), the technology has seen a great progress. The two main standard setting bodies in this regard are ITU and International Organization for Standardization (ISO). Recommendations of ITU include the standards like H 261/262/263/264 and these focus on applications in the area of telecommunication. Motion Pictures Experts Group (MPEG) of ISO has released standards like MPEG- 1/-2/-4 which focus the applications in computer and consumer electronics area. The standards defined by both of these groups have some parts in common and also some work has been performed as a joint venture. The field of video compression has been continuously developing with the en- hancements in the previous versions of the standards and introduction of new recommendations. MPEG-4 standard is followed in this thesis work. It can be easily said that video compression is a top requirement in any multimedia storage and transmission phenomenon with encoding the video in various forms before sending or storing it and then decoding it subse- quently at the receiver end or when viewing it. Besides the presence of digital video in television and CD/DVD etc, cellular phones will probably be the next high use place of video content. The limited storage capacity of mobile equipments dictates the requirement of efficient video compression tools. Video encoding in mobile equipments has developed from a high- end feature to something that is taken for granted. Nevertheless, it is a computationally demanding feature that requires well designed and well developed algorithms. Many different algorithms need to be evaluated in order to come closer to the optimal solution. As early as 1929, Ray Davis Kell described a form of video compression for which he obtained a patent [1]. Given the fact that a video is actually a series of pictures transmitted at some designated speed between successive images, Rays patent gave rise to the idea of transmitting the difference be- tween the successive images instead of sending the whole image. However, 1 Chapter 1. Introduction it took ages to get the idea implemented into reality but still it is a keystone of many video compression standards today. Connected to this idea, there comes the concept of motion estimation which tries to exploit the presence of temporal correlation at different positions between the video frames. It predicts the motion found in the current frame using already encoded frames. Henceforth, the residual frame contains much less energy than the actual frame. Motion vectors and the residual frame are encoded by a bit rate much lesser than the bit rate required to encode a regular frame. Motion estimation may require tremendous amount of computational work inside the video coding process. There are certain algorithms employed for doing motion estimation. The basic class of these is called Full Search Al- gorithms and it gives optimal performance but computationally very time consuming. To deal with this computation issue, many sub optimal fast search algorithms have been designed and this thesis will focus on some of them in a try to improve performance of one of them. The SLIMPEG motion estimation algorithm is taken as reference here and inherent video properties like spatial and temporal correlation has been applied to devise techniques in a try to achieve less complex yet performance oriented motion estimation algorithms. The rest of the report is organized as: Chapter 2 and chapter 3 deal with fundamentals of digital video and video compression respectively. Imple- mentation aspects of motion estimation have been explored in chapter 4 ending with the introduction of SLIMPEG motion estimation algorithm. Rate distortion and delta PSNR are the contents of chapter 5. Results of the main contribution have been provided in chapter 6 with their descrip- tion. Chapter 7 contains conclusion and some hints about future work in the field. 2 Chapter 2 Basics of Digital Video A video image is obtained by capturing the 2D plane view of a 3D scene. Digital video is then spatial and temporal sampled frames presented in a sequence. The spatio-temporal sampling unit which is usually called pixel (picture element) can be represented by a digital value to describe its color and brightness. The more sampling points taken to form the video frame the higher is usually the visual quality but requiring high storage capacity. The video frame is usually formed in a rectangular shape. The smoothness of a video is determined by the rate at which its frames are presented in a succession. A video comprising a frame rate of thirty frames per second looks fairly smooth enough for most purposes. A general comparison of ap- pearance of a video determined by its frame rate is given in the table 2.1[2]. Table 2.1: Video frame rates.[2] Video frame rates. Appearance Below 10 frames per second ’Jerky’, unnatural appearance to movement 10-20 frames per second Slow movement appears OK; rapid movement is clearly jerky 20-30 frames per second Movement is reasonably smooth 50-60 frames per second Movement
Load more

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]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Motion Vector Forecast and Mapping (MV-Fmap) Method for Entropy Coding Based Video Coders Julien Le Tanou, Jean-Marc Thiesse, Joël Jung, Marc Antonini
    Motion Vector Forecast and Mapping (MV-FMap) Method for Entropy Coding based Video Coders Julien Le Tanou, Jean-Marc Thiesse, Joël Jung, Marc Antonini To cite this version: Julien Le Tanou, Jean-Marc Thiesse, Joël Jung, Marc Antonini. Motion Vector Forecast and Mapping (MV-FMap) Method for Entropy Coding based Video Coders. MMSP’10 2010 IEEE International Workshop on Multimedia Signal Processing, Oct 2010, Saint Malo, France. pp.206. hal-00531819 HAL Id: hal-00531819 https://hal.archives-ouvertes.fr/hal-00531819 Submitted on 3 Nov 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Motion Vector Forecast and Mapping (MV-FMap) Method for Entropy Coding based Video Coders Julien Le Tanou #1, Jean-Marc Thiesse #2, Joël Jung #3, Marc Antonini ∗4 # Orange Labs 38 rue du G. Leclerc, 92794 Issy les Moulineaux, France 1 [email protected] {2 jeanmarc.thiesse,3 joelb.jung}@orange-ftgroup.com ∗ I3S Lab. University of Nice-Sophia Antipolis/CNRS 2000 route des Lucioles, 06903 Sophia Antipolis, France 4 [email protected] Abstract—Since the finalization of the H.264/AVC standard between motion vectors of neighboring frames and blocks, we and in order to meet the target set by both ITU-T and MPEG propose in this paper a method for motion vector coding based to define a new standard that reaches 50% bit rate reduction on a motion vector residuals forecast followed by an adaptive compared to H.264/AVC, many tools have efficiently improved the texture coding and the motion compensation accuracy.
    [Show full text]
  • 11.2 Motion Estimation and Motion Compensation 421
    11.2 Motion Estimation and Motion Compensation 421 vertical component to the enhancement filter, making the overall filter separable with 3 3 support. × 11.2 MOTION ESTIMATION AND MOTION COMPENSATION Motion compensation (MC) is very useful in video filtering to remove noise and enhance signal. It is useful since it allows the filter or coder to process through the video on a path of near-maximum correlation based on following motion trajectories across the frames making up the image sequence or video. Motion compensation is also employed in all distribution-quality video coding formats, since it is able to achieve the smallest prediction error, which is then easier to code. Motion can be characterized in terms of either a velocity vector v or displacement vector d and is used to warp a reference frame onto a target frame. Motion estimation is used to obtain these displacements, one for each pixel in the target frame. Several methods of motion estimation are commonly used: • Block matching • Hierarchical block matching • Pel-recursive motion estimation • Direct optical flow methods • Mesh-matching methods Optical flow is the apparent displacement vector field d .d1,d2/ we get from setting (i.e., forcing) equality in the so-called constraint equationD x.n1,n2,n/ x.n1 d1,n2 d2,n 1/. (11.2–1) D − − − All five approaches start from this basic equation, which is really just an ide- alization. Departures from the ideal are caused by the covering and uncovering of objects in the viewed scene, lighting variation both in time and across the objects in the scene, movement toward or away from the camera, as well as rotation about an axis (i.e., 3-D motion).
    [Show full text]
  • Diapositivo 1
    VC 14/15 – TP16 Video Compression Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline • The need for compression • Types of redundancy • Image compression • Video compression VC 14/15 - TP16 - Video Compression Topic: The need for compression • The need for compression • Types of redundancy • Image compression • Video compression VC 14/15 - TP16 - Video Compression Images are great! VC 14/15 - TP16 - Video Compression But... Images need storage space... A lot of space! Size: 1024 x 768 pixels RGB colour space 8 bits per color = 2,6 MBytes VC 14/15 - TP16 - Video Compression What about video? • VGA: 640x480, 3 bytes per pixel -> 920KB per image. • Each second of video: 23 MB • Each hour of vídeo: 83 GB The death of Digital Video VC 14/15 - TP16 - Video Compression What if... ? • We exploit redundancy to compress image and video information? – Image Compression Standards – Video Compression Standards • “Explosion” of Digital Image & Video – Internet media – DVDs – Digital TV – ... VC 14/15 - TP16 - Video Compression Compression • Data compression – Reduce the quantity of data needed to store the same information. – In computer terms: Use fewer bits. • How is this done? – Exploit data redundancy. • But don’t we lose information? – Only if you want to... VC 14/15 - TP16 - Video Compression Types of Compression • Lossy • Lossless – We do not obtain an – We obtain an exact exact copy of our copy of our compressed data after compressed data after decompression. decompression. – Very high compression – Lower compression rates. rates. – Increased degradation – Freely compress / with sucessive decompress images. compression / It all depends on what we decompression.
    [Show full text]
  • Motion Compensation on DCT Domain
    EURASIP Journal on Applied Signal Processing 2001:3, 147–162 © 2001 Hindawi Publishing Corporation Motion Compensation on DCT Domain Ut-Va Koc Lucent Technologies Bell Labs, 600 Mountain Avenue, Murray Hill, NJ 07974, USA Email: [email protected] K. J. Ray Liu Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD 20742, USA Email: [email protected] Received 21 May 2001 and in revised form 21 September 2001 Alternative fully DCT-based video codec architectures have been proposed in the past to address the shortcomings of the conven- tional hybrid motion compensated DCT video codec structures traditionally chosen as the basis of implementation of standard- compliant codecs. However, no prior effort has been made to ensure interoperability of these two drastically different architectures so that fully DCT-based video codecs are fully compatible with the existing video coding standards. In this paper, we establish the criteria for matching conventional codecs with fully DCT-based codecs. We find that the key to this interoperability lies in the heart of the implementation of motion compensation modules performed in the spatial and transform domains at both the encoder and the decoder. Specifically,if the spatial-domain motion compensation is compatible with the transform-domain motion compensation, then the states in both the coder and the decoder will keep track of each other even after a long series of P-frames. Otherwise, the states will diverge in proportion to the number of P-frames between two I-frames. This sets an important criterion for the development of any DCT-based motion compensation schemes.
    [Show full text]
  • A Dynamic Motion Vector Referencing Scheme for Video Coding
    A DYNAMIC MOTION VECTOR REFERENCING SCHEME FOR VIDEO CODING Jingning Han, Yaowu Xu, and James Bankoski WebM Codec Team, Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043 Emails: fjingning,yaowu,[email protected] ABSTRACT poral neighbors. Prior research exploits such correlations to improve coding efficiency [2]-[4]. Video codecs exploit temporal redundancy in video signals, direct mode through the use of motion compensated prediction, to achieve A derived motion vector mode named is pro- superior compression performance. The coding of motion posed in [5] for bi-directional predicted blocks. Unlike the vectors takes a large portion of the total rate cost. Prior re- conventional inter block that needs to send the motion vector search utilizes the spatial and temporal correlation of the mo- residual to the decoder, this mode only infers motion vector tion field to improve the coding efficiency of the motion in- from previously coded blocks. To determine the motion vec- formation. It typically constructs a candidate pool composed tor for each reference frame, the scheme checks the neigh- of a fixed number of reference motion vectors and allows the boring blocks in the order of above, left, top-right, and top- codec to select and reuse the one that best approximates the left, picks the first one that has the same reference frame, and motion of the current block. This largely disconnects the en- reuses its motion vector. A rate-distortion optimization ap- tropy coding process from the block’s motion information, proach that allows the codec to select between two reference and throws out any information related to motion consistency, motion vector candidates is proposed in [6].
    [Show full text]
  • COMPRESSIVE TECHNIQUES APPLICABLE for VIDEO COMPRESSION Jayshree R
    COMPRESSIVE TECHNIQUES APPLICABLE FOR VIDEO COMPRESSION Jayshree R. Pansare1, Ketki R. Jadhav2 1,2 Department of Computer Engineering, M.E.S. College of Engineering, Pune, India Abstract there lot of techniques for compression of Communication over internet is becoming images/ videos, finding better compression important part of our lives. Now days techniques for diversified applications or millions of peoples spend their time on you improving performance of existing techniques is tube, video communication and video still concern of lot of researchers. conferencing etc. Videos require large Images/videos are either get compressed by amount of bandwidth and transmission time. using lossless compression methods consist of Lot of video compression standards, Run length encoding (RLE), LZW ( Lempel Ziv- techniques and algorithms had been Welch) Coding, Huffman coding, Area Coding developed to reduce data quantity and gain or using lossy compression techniques consist of expected quality as possible as can. Video Transformation coding, Fractal segmentation, compressions technologies are now become Vector quantization, Sub band coding and Block the important part of the way we create, truncation coding. Even though most of us want consume and communicate visual image representation of content rather than text information. Paper reviews no of video for better understanding and visual appearance, compression techniques. It divides those videos turned out to be standards now days and techniques based on used techniques and used used by lot of applications such as DVD, digital concepts in survey papers. And shows that for TV, HDTV, Video calls and teleconferencing. which type of videos these techniques are Because of lot of advancement in network useful.
    [Show full text]
  • Lecture 13 Review Questions.Pdf
    Multimedia communications ECP 610 Omar A. Nasr [email protected] May, 2015 1 Take home messages from the course: Google, Facebook, Microsoft and other content providers would like to take over the whole stack! Google 2 3 4 5 6 7 Multimedia communications involves: Media coding (Speech, Audio, Images, Video coding) Media transmission Media coding (compression) Production models (For speech, music, image, video) Perception models Auditory system Masking Ear sensitivity to different frequency ranges Visual system Brightness vs color Low frequency vs High frequency Quality of Service and Perception QoS for voice QoS for video 8 Speech production system 9 10 Perception system 11 12 Digital Image Representation (3 Bit Quantization) CS 414 - Spring 2009 Image Representations Black and white image single color plane with 1 bits Gray scale image single color plane with 8 bits Color image three color planes each with 8 bits RGB, CMY, YIQ, etc. Indexed color image single plane that indexes a color table Compressed images TIFF, JPEG, BMP, etc. 4 gray levels 2gray levels Image Representation Example 24 bit RGB Representation (uncompressed) 128 135 166 138 190 132 129 255 105 189 167 190 229 213 134 111 138 187 128 138 135 190 166 132 129 189 255 167 105 190 229 111 213 138 134 187 Color Planes Techniques used in coding: Loss-Less compression : Huffman coding Variable length, prefix, uniquely decodable code Main objective: optimally assign different number of bits to symbols having different frequencies 16 Discrete
    [Show full text]
  • NEW METHODS for MOTION ESTIMATION with APPLICATIONS to LOW COMPLEXITY VIDEO COMPRESSION a Thesis Submitted to the Faculty Of
    NEW METHODS FOR MOTION ESTIMATION WITH APPLICATIONS TO LOW COMPLEXITY VIDEO COMPRESSION A Thesis Submitted to the Faculty of Purdue University by Zhen Li In Partial Ful¯llment of the Requirements for the Degree of Doctor of Philosophy December 2005 ii To my parents, Meilian Jin and Yanzong Li; To my wife, Limin Liu; and in memory of my grandmother, Zhugu Cai. iii ACKNOWLEDGMENTS I would like to thank my advisor, Professor Edward J. Delp, for his guidance and support. I truly appreciate the research freedom he allowed. And I am deeply grateful for his reminder to keep focused when I was zigzagging with various research topics, and his advice to look deeper into one challenging problem rather than playing around with many problems. I would also like to thank my Doctoral Committee: Professors Charles A. Bouman, Michael D. Zoltowski, and Dongyan Xu, for their advice, encouragement, and in- sights. I am grateful to the organizations which supported the research in this disser- tation, in particular the Indiana Twenty-First Century Research and Technology Fund. I appreciate the support and friendship of all my colleagues in the Video and Image Processing (VIPER) lab. It has been a joyful and memorable journey with these excellent people and scholars: Dr. Eduardo Asbun, Dr. Gregory Cook, Dr. Paul Salama, Dr. Lauren Christopher, Dr. Eugene Lin, Dr. Zoe Yuxin Liu, Dr. Yajie Sun, Dr. Jinwha Yang, Dr. Cuneyt Taskiran, Dr. Sahng-Gyu Park, Hyung Cook Kim, Jennifer Talavage, Hwayoung Um, Anthony Martone, Aravind Mikkili- neni, Oriol Guitart, Michael Igarta, Liang Liang, Ying Chen, Fengqing Zhu, Ashok Mariappan and Hakeem Ogunleye.
    [Show full text]
  • Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang Abstract—Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize target frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that
    [Show full text]