Motion compensation and very low bit rate video coding

Recently, many activities of the International Telecommunication Union (ITU) and the International Standard Organization (ISO) are leading to define new standards for very low bit-rate video coding, such as H.263 and MPEG-4 after successful applications of the international standards H.261 and MPEG-1/2 for video coding above 64kbps. However, at very low bit-rate the classic block matching based DCT video coding scheme suffers seriously from blocking artifacts which degrade the quality of reconstructed video frames considerably. To solve this problem, a new technique in which motion compensation is based on dense motion field is presented in this dissertation. Four efficient new video coding algorithms based on this new technique for very low bit-rate are proposed. (1) After studying model-based video coding algorithms, we propose an optical flow based video coding algorithm with thresh-olding techniques. A statistic model is established for distribution of intensity difference between two successive frames, and four thresholds are used to control the bit-rate and the quality of reconstructed frames. It outperforms the typical model-based techniques in terms of complexity and quality of reconstructed frames. (2) An efficient algorithm using DCT coded optical flow. It is found that dense motion fields can be modeled as the first order auto-regressive model, and efficiently compressed with DCT technique, hence achieving very low bit-rate and higher visual quality than the H.263/TMN5. (3) A region-based discrete wavelet transform video coding algorithm. This algorithm implements dense motion field and regions are segmented according to their content significance. The DWT is applied to residual images region by region, and bits are adaptively allocated to regions. It improves the visual quality and PSNR of significant regions while maintaining low bit-rate. (4) A segmentation-based video coding algorithm for stereo sequence. A correlation-feedback algorithm with Kalman filter is utilized to improve the accuracy of optical flow fields. Three criteria, which are associated with 3-D information, 2-D connectivity and motion vector fields, respectively, are defined for object segmentation. A chain code is utilized to code the shapes of the segmented objects. it can achieve very high compression ratio up to several thousands

비디오 프레임 보간을 위한 다중 벡터 기반의 MEMC 및 심층 CNN

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 이혁재.Block-based hierarchical motion estimations are widely used and are successful in generating high-quality interpolation. However, it still fails in the motion estimation of small objects when a background region moves in a different direction. This is because the motion of small objects is neglected by the down-sampling and over-smoothing operations at the top level of image pyramids in the maximum a posterior (MAP) method. Consequently, the motion vector of small objects cannot be detected at the bottom level, and therefore, the small objects often appear deformed in an interpolated frame. This thesis proposes a novel algorithm that preserves the motion vector of the small objects by adding a secondary motion vector candidate that represents the movement of the small objects. This additional candidate is always propagated from the top to the bottom layers of the image pyramid. Experimental results demonstrate that the intermediate frame interpolated by the proposed algorithm significantly improves the visual quality when compared with conventional MAP-based frame interpolation. In motion compensated frame interpolation, a repetition pattern in an image makes it difficult to derive an accurate motion vector because multiple similar local minima exist in the search space of the matching cost for motion estimation. In order to improve the accuracy of motion estimation in a repetition region, this thesis attempts a semi-global approach that exploits both local and global characteristics of a repetition region. A histogram of the motion vector candidates is built by using a voter based voting system that is more reliable than an elector based voting system. Experimental results demonstrate that the proposed method significantly outperforms the previous local approach in term of both objective peak signal-to-noise ratio (PSNR) and subjective visual quality. In video frame interpolation or motion-compensated frame rate up-conversion (MC-FRUC), motion compensation along unidirectional motion trajectories directly causes overlaps and holes issues. To solve these issues, this research presents a new algorithm for bidirectional motion compensated frame interpolation. Firstly, the proposed method generates bidirectional motion vectors from two unidirectional motion vector fields (forward and backward) obtained from the unidirectional motion estimations. It is done by projecting the forward and backward motion vectors into the interpolated frame. A comprehensive metric as an extension of the distance between a projected block and an interpolated block is proposed to compute weighted coefficients in the case when the interpolated block has multiple projected ones. Holes are filled based on vector median filter of non-hole available neighbor blocks. The proposed method outperforms existing MC-FRUC methods and removes block artifacts significantly. Video frame interpolation with a deep convolutional neural network (CNN) is also investigated in this thesis. Optical flow and video frame interpolation are considered as a chicken-egg problem such that one problem affects the other and vice versa. This thesis presents a stack of networks that are trained to estimate intermediate optical flows from the very first intermediate synthesized frame and later the very end interpolated frame is generated by the second synthesis network that is fed by stacking the very first one and two learned intermediate optical flows based warped frames. The primary benefit is that it glues two problems into one comprehensive framework that learns altogether by using both an analysis-by-synthesis technique for optical flow estimation and vice versa, CNN kernels based synthesis-by-analysis. The proposed network is the first attempt to bridge two branches of previous approaches, optical flow based synthesis and CNN kernels based synthesis into a comprehensive network. Experiments are carried out with various challenging datasets, all showing that the proposed network outperforms the state-of-the-art methods with significant margins for video frame interpolation and the estimated optical flows are accurate for challenging movements. The proposed deep video frame interpolation network to post-processing is applied to the improvement of the coding efficiency of the state-of-art video compress standard, HEVC/H.265 and experimental results prove the efficiency of the proposed network.블록 기반 계층적 움직임 추정은 고화질의 보간 이미지를 생성할 수 있어 폭넓게 사용되고 있다. 하지만, 배경 영역이 움직일 때, 작은 물체에 대한 움직임 추정 성능은 여전히 좋지 않다. 이는 maximum a posterior (MAP) 방식으로 이미지 피라미드의 최상위 레벨에서 down-sampling과 over-smoothing으로 인해 작은 물체의 움직임이 무시되기 때문이다. 결과적으로 이미지 피라미드의 최하위 레벨에서 작은 물체의 움직임 벡터는 검출될 수 없어 보간 이미지에서 작은 물체는 종종 변형된 것처럼 보인다. 본 논문에서는 작은 물체의 움직임을 나타내는 2차 움직임 벡터 후보를 추가하여 작은 물체의 움직임 벡터를 보존하는 새로운 알고리즘을 제안한다. 추가된 움직임 벡터 후보는 항상 이미지 피라미드의 최상위에서 최하위 레벨로 전파된다. 실험 결과는 제안된 알고리즘의 보간 생성 프레임이 기존 MAP 기반 보간 방식으로 생성된 프레임보다 이미지 화질이 상당히 향상됨을 보여준다. 움직임 보상 프레임 보간에서, 이미지 내의 반복 패턴은 움직임 추정을 위한 정합 오차 탐색 시 다수의 유사 local minima가 존재하기 때문에 정확한 움직임 벡터 유도를 어렵게 한다. 본 논문은 반복 패턴에서의 움직임 추정의 정확도를 향상시키기 위해 반복 영역의 local한 특성과 global한 특성을 동시에 활용하는 semi-global한 접근을 시도한다. 움직임 벡터 후보의 히스토그램은 선거 기반 투표 시스템보다 신뢰할 수 있는 유권자 기반 투표 시스템 기반으로 형성된다. 실험 결과는 제안된 방법이 이전의 local한 접근법보다 peak signal-to-noise ratio (PSNR)와 주관적 화질 판단 관점에서 상당히 우수함을 보여준다. 비디오 프레임 보간 또는 움직임 보상 프레임율 상향 변환 (MC-FRUC)에서, 단방향 움직임 궤적에 따른 움직임 보상은 overlap과 hole 문제를 일으킨다. 본 연구에서 이러한 문제를 해결하기 위해 양방향 움직임 보상 프레임 보간을 위한 새로운 알고리즘을 제시한다. 먼저, 제안된 방법은 단방향 움직임 추정으로부터 얻어진 두 개의 단방향 움직임 영역(전방 및 후방)으로부터 양방향 움직임 벡터를 생성한다. 이는 전방 및 후방 움직임 벡터를 보간 프레임에 투영함으로써 수행된다. 보간된 블록에 여러 개의 투영된 블록이 있는 경우, 투영된 블록과 보간된 블록 사이의 거리를 확장하는 기준이 가중 계수를 계산하기 위해 제안된다. Hole은 hole이 아닌 이웃 블록의 vector median filter를 기반으로 처리된다. 제안 방법은 기존의 MC-FRUC보다 성능이 우수하며, 블록 열화를 상당히 제거한다. 본 논문에서는 CNN을 이용한 비디오 프레임 보간에 대해서도 다룬다. Optical flow 및 비디오 프레임 보간은 한 가지 문제가 다른 문제에 영향을 미치는 chicken-egg 문제로 간주된다. 본 논문에서는 중간 optical flow 를 계산하는 네트워크와 보간 프레임을 합성 하는 두 가지 네트워크로 이루어진 하나의 네트워크 스택을 구조를 제안한다. The final 보간 프레임을 생성하는 네트워크의 경우 첫 번째 네트워크의 출력인 보간 프레임 와 중간 optical flow based warped frames을 입력으로 받아서 프레임을 생성한다. 제안된 구조의 가장 큰 특징은 optical flow 계산을 위한 합성에 의한 분석법과 CNN 기반의 분석에 의한 합성법을 모두 이용하여 하나의 종합적인 framework로 결합하였다는 것이다. 제안된 네트워크는 기존의 두 가지 연구인 optical flow 기반 프레임 합성과 CNN 기반 합성 프레임 합성법을 처음 결합시킨 방식이다. 실험은 다양하고 복잡한 데이터 셋으로 이루어졌으며, 보간 프레임 quality 와 optical flow 계산 정확도 측면에서 기존의 state-of-art 방식에 비해 월등히 높은 성능을 보였다. 본 논문의 후 처리를 위한 심층 비디오 프레임 보간 네트워크는 코딩 효율 향상을 위해 최신 비디오 압축 표준인 HEVC/H.265에 적용할 수 있으며, 실험 결과는 제안 네트워크의 효율성을 입증한다.Abstract i Table of Contents iv List of Tables vii List of Figures viii Chapter 1. Introduction 1 1.1. Hierarchical Motion Estimation of Small Objects 2 1.2. Motion Estimation of a Repetition Pattern Region 4 1.3. Motion-Compensated Frame Interpolation 5 1.4. Video Frame Interpolation with Deep CNN 6 1.5. Outline of the Thesis 7 Chapter 2. Previous Works 9 2.1. Previous Works on Hierarchical Block-Based Motion Estimation 9 2.1.1. Maximum a Posterior (MAP) Framework 10 2.1.2.Hierarchical Motion Estimation 12 2.2. Previous Works on Motion Estimation for a Repetition Pattern Region 13 2.3. Previous Works on Motion Compensation 14 2.4. Previous Works on Video Frame Interpolation with Deep CNN 16 Chapter 3. Hierarchical Motion Estimation for Small Objects 19 3.1. Problem Statement 19 3.2. The Alternative Motion Vector of High Cost Pixels 20 3.3. Modified Hierarchical Motion Estimation 23 3.4. Framework of the Proposed Algorithm 24 3.5. Experimental Results 25 3.5.1. Performance Analysis 26 3.5.2. Performance Evaluation 29 Chapter 4. Semi-Global Accurate Motion Estimation for a Repetition Pattern Region 32 4.1. Problem Statement 32 4.2. Objective Function and Constrains 33 4.3. Elector based Voting System 34 4.4. Voter based Voting System 36 4.5. Experimental Results 40 Chapter 5. Multiple Motion Vectors based Motion Compensation 44 5.1. Problem Statement 44 5.2. Adaptive Weighted Multiple Motion Vectors based Motion Compensation 45 5.2.1. One-to-Multiple Motion Vector Projection 45 5.2.2. A Comprehensive Metric as the Extension of Distance 48 5.3. Handling Hole Blocks 49 5.4. Framework of the Proposed Motion Compensated Frame Interpolation 50 5.5. Experimental Results 51 Chapter 6. Video Frame Interpolation with a Stack of Deep CNN 56 6.1. Problem Statement 56 6.2. The Proposed Network for Video Frame Interpolation 57 6.2.1. A Stack of Synthesis Networks 57 6.2.2. Intermediate Optical Flow Derivation Module 60 6.2.3. Warping Operations 62 6.2.4. Training and Loss Function 63 6.2.5. Network Architecture 64 6.2.6. Experimental Results 64 6.2.6.1. Frame Interpolation Evaluation 64 6.2.6.2. Ablation Experiments 77 6.3. Extension for Quality Enhancement for Compressed Videos Task 83 6.4. Extension for Improving the Coding Efficiency of HEVC based Low Bitrate Encoder 88 Chapter 7. Conclusion 94 References 97Docto

On the formulation and uses of SVD-based generalized curvatures

2016 Summer.Includes bibliographical references.In this dissertation we consider the problem of computing generalized curvature values from noisy, discrete data and applications of the provided algorithms. We first establish a connection between the Frenet-Serret Frame, typically defined on an analytical curve, and the vectors from the local Singular Value Decomposition (SVD) of a discretized time-series. Next, we expand upon this connection to relate generalized curvature values, or curvatures, to a scaled ratio of singular values. Initially, the local singular value decomposition is centered on a point of the discretized time-series. This provides for an efficient computation of curvatures when the underlying curve is known. However, when the structure of the curve is not known, for example, when noise is present in the tabulated data, we propose two modifications. The first modification computes the local singular value decomposition on the mean-centered data of a windowed selection of the time-series. We observe that the mean-center version increases the stability of the curvature estimations in the presence of signal noise. The second modification is an adaptive method for selecting the size of the window, or local ball, to use for the singular value decomposition. This allows us to use a large window size when curvatures are small, which reduces the effects of noise thanks to the use of a large number of points in the SVD, and to use a small window size when curvatures are large, thereby best capturing the local curvature. Overall we observe that adapting the window size to the data, enhances the estimates of generalized curvatures. The combination of these two modifications produces a tool for computing generalized curvatures with reasonable precision and accuracy. Finally, we compare our algorithm, with and without modifications, to existing numerical curvature techniques on different types of data such as that from the Microsoft Kinect 2 sensor. To address the topic of action segmentation and recognition, a popular topic within the field of computer vision, we created a new dataset from this sensor showcasing a pose space skeletonized representation of individuals performing continuous human actions as defined by the MSRC-12 challenge. When this data is optimally projected onto a low-dimensional space, we observed each human motion lies on a distinguished line, plane, hyperplane, etc. During transitions between motions, either the dimension of the optimal subspace significantly, or the trajectory of the curve through pose space nearly reverses. We use our methods of computing generalized curvature values to identify these locations, categorized as either high curvatures or changing curvatures. The geometric characterization of the time-series allows us to segment individual,or geometrically distinct, motions. Finally, using these segments, we construct a methodology for selecting motions to conjoin for the task of action classification

Motion Offset for Blur Modeling

Motion blur caused by the relative movement between the camera and the subject is often an undesirable degradation of the image quality. In most conventional deblurring methods, a blur kernel is estimated for image deconvolution. Due to the ill-posed nature, predefined priors are proposed to suppress the ill-posedness. However, these predefined priors can only handle some specific situations. In order to achieve a better deblurring performance on dynamic scene, deep-learning based methods are proposed to learn a mapping function that restore the sharp image from a blurry image. The blur may be implicitly modelled in feature extraction module. However, the blur modelled from the paired dataset cannot be well generalized to some real-world scenes. To summary, an accurate and dynamic blur model that more closely approximates real-world blur is needed. By revisiting the principle of camera exposure, we can model the blur with the displacements between sharp pixels and the exposed pixel, namely motion offsets. Given specific physical constraints, motion offsets are able to form different exposure trajectories (i.e. linear, quadratic). Compare to conventional blur kernel, our proposed motion offsets are a more rigorous approximation for real-world blur, since they can constitute a non-linear and non-uniform motion field. Through learning from dynamic scene dataset, an accurate and spatial-variant motion offset field is obtained. With accurate motion information and a compact blur modeling method, we explore the ways of utilizing motion information to facilitate multiple blur-related tasks. By introducing recovered motion offsets, we build up a motion-aware and spatial-variant convolution. For extracting a video clip from a blurry image, motion offsets can provide an explicit (non-)linear motion trajectory for interpolating. We also work towards a better image deblurring performance in real-world scenarios by improving the generalization ability of the deblurring model

Research and developments of distributed video coding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The recent developed Distributed Video Coding (DVC) is typically suitable for the applications such as wireless/wired video sensor network, mobile camera etc. where the traditional video coding standard is not feasible due to the constrained computation at the encoder. With DVC, the computational burden is moved from encoder to decoder. The compression efficiency is achieved via joint decoding at the decoder. The practical application of DVC is referred to Wyner-Ziv video coding (WZ) where the side information is available at the decoder to perform joint decoding. This join decoding inevitably causes a very complex decoder. In current WZ video coding issues, many of them emphasise how to improve the system coding performance but neglect the huge complexity caused at the decoder. The complexity of the decoder has direct influence to the system output. The beginning period of this research targets to optimise the decoder in pixel domain WZ video coding (PDWZ), while still achieves similar compression performance. More specifically, four issues are raised to optimise the input block size, the side information generation, the side information refinement process and the feedback channel respectively. The transform domain WZ video coding (TDWZ) has distinct superior performance to the normal PDWZ due to the exploitation in spatial direction during the encoding. However, since there is no motion estimation at the encoder in WZ video coding, the temporal correlation is not exploited at all at the encoder in all current WZ video coding issues. In the middle period of this research, the 3D DCT is adopted in the TDWZ to remove redundancy in both spatial and temporal direction thus to provide even higher coding performance. In the next step of this research, the performance of transform domain Distributed Multiview Video Coding (DMVC) is also investigated. Particularly, three types transform domain DMVC frameworks which are transform domain DMVC using TDWZ based 2D DCT, transform domain DMVC using TDWZ based on 3D DCT and transform domain residual DMVC using TDWZ based on 3D DCT are investigated respectively. One of the important applications of WZ coding principle is error-resilience. There have been several attempts to apply WZ error-resilient coding for current video coding standard e.g. H.264/AVC or MEPG 2. The final stage of this research is the design of WZ error-resilient scheme for wavelet based video codec. To balance the trade-off between error resilience ability and bandwidth consumption, the proposed scheme emphasises the protection of the Region of Interest (ROI) area. The efficiency of bandwidth utilisation is achieved by mutual efforts of WZ coding and sacrificing the quality of unimportant area. In summary, this research work contributed to achieves several advances in WZ video coding. First of all, it is targeting to build an efficient PDWZ with optimised decoder. Secondly, it aims to build an advanced TDWZ based on 3D DCT, which then is applied into multiview video coding to realise advanced transform domain DMVC. Finally, it aims to design an efficient error-resilient scheme for wavelet video codec, with which the trade-off between bandwidth consumption and error-resilience can be better balanced

The Second-Generation Guide Star Catalog: Description and Properties

The GSC-II is an all-sky database of objects derived from the uncompressed DSS that the STScI has created from the Palomar and UK Schmidt survey plates and made available to the community. Like its predecessor (GSC-I), the GSC-II was primarily created to provide guide star information and observation planning support for HST. This version, however, is already employed at some of the ground-based new-technology telescopes such as GEMINI, VLT, and TNG, and will also be used to provide support for the JWST and Gaia space missions as well as LAMOST, one of the major ongoing scientific projects in China. Two catalogs have already been extracted from the GSC-II database and released to the astronomical community. A magnitude-limited (R=18.0) version, GSC2.2, was distributed soon after its production in 2001, while the GSC2.3 release has been available for general access since 2007. The GSC2.3 catalog described in this paper contains astrometry, photometry, and classification for 945,592,683 objects down to the magnitude limit of the plates. Positions are tied to the ICRS; for stellar sources, the all-sky average absolute error per coordinate ranges from 0.2" to 0.28" depending on magnitude. When dealing with extended objects, astrometric errors are 20% worse in the case of galaxies and approximately a factor of 2 worse for blended images. Stellar photometry is determined to 0.13-0.22 mag as a function of magnitude and photographic passbands (B,R,I). Outside of the galactic plane, stellar classification is reliable to at least 90% confidence for magnitudes brighter than R=19.5, and the catalog is complete to R=20.Comment: 52 pages, 33 figures, to be published in AJ August 200

Data mining based learning algorithms for semi-supervised object identification and tracking

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, and track objects with predictable high degrees of specificity and sensitivity. Automatic object detection and tracking algorithms face several obstacles, such as large and incomplete datasets, ill-defined regions of interest (ROIs), variable scalability, lack of compactness, angular regions, partial occlusions, environmental variables, and unknown potential object classes, which work against their ability to achieve accurate real-time results. Methods must produce fast and accurate results by streamlining image processing, data compression and reduction, feature extraction, classification, and tracking algorithms. Data mining techniques can sufficiently address these challenges by implementing efficient and accurate dimensionality reduction with feature extraction to refine incomplete (ill-partitioning) data-space and addressing challenges related to object classification, intra-class variability, and inter-class dependencies. A series of methods have been developed to combat many of the challenges for the purpose of creating a sensor exploitation and tracking framework for real time image sensor inputs. The framework has been broken down into a series of sub-routines, which work in both series and parallel to accomplish tasks such as image pre-processing, data reduction, segmentation, object detection, tracking, and classification. These methods can be implemented either independently or together to form a synergistic solution to object detection and tracking. The main contributions to the SE field include novel feature extraction methods for highly discriminative object detection, classification, and tracking. Also, a new supervised classification scheme is presented for detecting objects in urban environments. This scheme incorporates both novel features and non-maximal suppression to reduce false alarms, which can be abundant in cluttered environments such as cities. Lastly, a performance evaluation of Graphical Processing Unit (GPU) implementations of the subtask algorithms is presented, which provides insight into speed-up gains throughout the SE framework to improve design for real time applications. The overall framework provides a comprehensive SE system, which can be tailored for integration into a layered sensing scheme to provide the war fighter with automated assistance and support. As more sensor technology and integration continues to advance, this SE framework can provide faster and more accurate decision support for both intelligence and civilian applications

A Framework for Temperature Imaging using the Change in Backscattered Ultrasonic Signals

Hyperthermia is a cancer treatment that elevates tissue temperature to 40 to 43oC. It would benefit from a non-invasive, safe, inexpensive and convenient thermometry to monitor heating patterns. Ultrasound is a modality that meets these requirements. In our initial work, using both prediction and experimental data, we showed that the change in the backscattered energy: CBE) is a potential parameter for TI. CBE, however, was computed in a straightforward yet ad hoc manner. In this work, we developed and exploited a mathematical representation for our approach to TI to optimize temperature accuracy. Non-thermal effects of noise and motion confound the use of CBE. Assuming additive white Gaussian noise, we applied signal averaging and thresholding to reduce noise effects. Our motion compensation algorithms were also applied to images with known motion to evaluate factors affecting the compensation performance. In the framework development, temperature imaging was modeled as a problem of estimating temperature from the random processes resulting from thermal changes in signals. CBE computation was formalized as a ratio between two random variables. Mutual information: MI) was studied as an example of possible parameters for temperature imaging based on the joint distributions. Furthermore, a maximum likelihood estimator: MLE) was developed. Both simulations and experimental results showed that noise effects were reduced by signal averaging. The motion compensation algorithms proved to be able to compensate for motion in images and were improved by choosing appropriate interpolation methods and sample rates. For images of uniformly distributed scatterers, CBE and MI can be computed independent of SNR to improve the temperature accuracy. The application of the MLE also showed improvements in temperature accuracy compared to the energy ratio from the signal mean in simulations. The application of the framework to experimental data requires more work to implement noise reduction approaches in 3D heating experiments. The framework identified ways in which we were able to reduce the effects of both noise and motion. The framework formalized our approaches to temperature imaging, improved temperature accuracy in simulations, and can be applied to experimental data if the noise reduction approaches can be implemented for 3D experiments