Estimation of Subpixel Motion Using Bispectrum

This work includes some new aspects of motion estimation by the optic flow method in scale spaces. The usual techniques for motion estimation are limited to the application of coarse to fine strategies. The coarse to fine strategies can be successful only if there is enough information in every scale. In this work we investigate the motion estimation in the scale space more basically. The wavelet choice for scale space decomposition of image sequences is discussed in the first part of this work. We make use of the continuous wavelet transform with rotationally symmetric wavelets. Bandpass decomposed sequences allow the replacement of the structure tensor by the phase invariant energy operator. The structure tensor is computationally more expensive because of its spatial or spatio-temporal averaging. The energy operator needs in general no further averaging. The numerical accuracy of the motion estimation with the energy operator is compared to the results of usual techniques, based on the structure tensor. The comparison tests are performed on synthetic and real life sequences. Another practical contribution is the accuracy measurement for motion estimation by adaptive smoothed tensor fields. The adaptive smoothing relies on nonlinear anisotropic diffusion with discontinuity and curvature preservation. We reached an accuracy gain under properly chosen parameters for the diffusion filter. A theoretical contribution from mathematical point of view is a new discontinuity and curvature preserving regularization for motion estimation. The convergence of solutions for the isotropic case of the nonlocal partial differential equation is shown. For large displacements between two consecutive frames the optic flow method is systematically corrupted because of the violence of the sampling theorem. We developed a new method for motion analysis by scale decomposition, which allows to circumvent the systematic corruption without using the coarse to fine strategy. The underlying assumption is, that in a certain neighborhood the grey value undergoes the same displacement. If this is fulfilled, then the same optic flow should be measured in all scales. If there arise inconsistencies in a pixel across the scale space, so they can be detected and the scales containing this inconsistencies are not taken into account.

The H.264 video coding standard exhibits high performance in terms of compression and image quality compared to the other existing standard such as H.263, MPEGX. This improved performance is achieved due to the mainly enormous computations in multiple mode motion estimation and compensation. Recent research tried to reduce the computational time using predictive motion estimation, early zero motion vector detection, fast motion estimation, fast mode decision etc. These approaches successfully reduce the computational time by degrading the image quality. Phase correlation technique is used to find the shift between two pictures. In this paper we used phase correlation technique to indicate the motion information between current and reference block and then we devise an algorithm to predict the motion estimation block size. Using phase correlation we are able to successfully predict the motion estimation mode directly instead of using exhaustive motion estimation by all possible modes, thus we save a huge amount of computational time. The experimental results show that we can save around 50% time in motion estimation without degrading the image quality.

The key to high performance in image sequence coding lies in an efficient reduction of the temporal redundancies. For this purpose, motion estimation and compensation techniques have been successfully applied. This paper studies motion estimation algorithms in the context of first generation coding techniques commonly used in digital TV. In this framework, estimating the motion in the scene is not an intrinsic goal. Motion estimation should indeed provide good temporal prediction and simultaneously require low overhead information. More specifically the aim is to minimize globally the bandwidth corresponding to both the prediction error information and the motion parameters. This paper first clarifies the notion of motion, reviews classical motion estimation techniques, and outlines new perspectives. Block matching techniques are shown to be the most appropriate in the framework of first generation coding. To overcome the drawbacks characteristic of most block matching techniques, this paper proposes a new locally adaptive multigrid block matching motion estimation technique. This algorithm has been designed taking into account the above aims. It leads to a robust motion field estimation precise prediction along moving edges and a decreased amount of side information in uniform areas. Furthermore, the algorithm controls the accuracy of the motion estimation procedure in order to optimally balance the amount of information corresponding to the prediction error and to the motion parameters. Experimental results show that the technique results in greatly enhanced visual quality and significant saving in terms of bit rate when compared to classical block matching techniques. >

Motion and structure estimation are elementary problems of computer vision. These are active areas of research, even though the first methods were proposed several decades ago. We develop new approaches for motion and structure estimation for autonomous driving. An autonomous vehicle requires an accurate model of its environment, wrong decisions made by an autonomous car can have severe consequences. We assume the monocular setup, where only a single camera is mounted on the car. Outdoor traffic sequences are challenging for optical flow estimation. The high speed of the car causes large displacements in the optical flow field, the lighting conditions are unstable and there can be strong distortions due to reflections and difficult weather conditions. We propose new discrete methods, which determine optical flow as optimal configuration of probabilistic graphical models. The first approach selects sparse locations in the reference frame, and matches them across the second image. The best correspondences, which match constraints from a multiple view configuration, are considered motion vectors in a graphical model. In a second approach, we solve for dense optical flow by approximating the original infeasible graphical model with a sequence of reduced models. The monocular configuration poses challenges to the estimation of scene structure, camera positions and scene parameters need to be estimated jointly. The geometry of multiple views creates blind spots in the images, and adds a scale ambiguity, which both to not exist in a setup with multiple cameras. We propose two methods for structure estimation. The first approach determines the energy optimal camera track, given optical flow and depth observations. A further approach estimates camera motion and a piecewise planar scene description jointly from a single optical flow field. The scene description contains depth and plane normal information. We evaluate our approaches for motion and structure estimation on different real world and rendered datasets. In addition to evaluation on publicly available evaluation data, we evaluate on a new rendered dataset with ground truth plane normals.

This article describes the aircraft image recognition system implemented using the phase correlation technique in Matlab environment. The phase correlation is computed by using the normalized cross-power spectrum between the database and the template test image. The main objective of this article is to develop methods for static analysis of aircraft images. An unknown fighter aircraft is recognized by comparing its static image with those from a database of images of aircraft. This work is a research initiative involving the use of image processing techniques to detect three-dimensional (3D) aircraft object based on their 2D images, providing feedback information for strategic purposes. The phase correlation technique is found to give a better recognition result for the set of database and test images considered, compared with the invariant moments. The phase correlation method is also used in other areas such as image registration. Aircraft images used include those from AeroIndia 2011 held at Bangalore, India.

This paper addresses the problem of rigid motion estimation and 3D reconstruction in vision systems where it is possible to recover the incident light ray direction from the image points. Such systems include pinhole cameras and catadioptric cameras. Given two images of the same scene acquired from two different positions, the transformation is estimated by means of an iterative process. The estimation process aims at having corresponding incident rays intersecting at the same 3D point. Geometrical relationships are derived to support the estimation method. Furthermore, this paper also addresses the problem of the mapping from 3D points to image points, for non-central catadioptric cameras with mirror surfaces given by quadrics. The projection model presented can be expressed in a non-linear equation of only one variable, being more stable and easier to solve than the classical Snell's law. Experiments with real images are presented, by using simulated annealing as estimation method.

This dissertation reports on rate-distortion optimized and computation-constrained video coding algorithms which yield effective and efficient block-based video encoders. We first propose a new cost function for motion estimation which can achieve good tradeoffs among motion vector bit rate, compensation distortion, and number of computations. Based on the new cost function, we propose a predictive motion estimation algorithm which can effectively determine the size of the search region according to the statistics of the motion field. The proposed motion estimation algorithm allows control of the motion vector bit rate and the computational cost, simultaneously. We also develop two efficient rate-distortion optimized coding mode selection algorithms, based on thresholding and finite state machine (FSM) methods, respectively. The thresholding method can eliminate consideration of the computationally expensive modes. The FSM method determines the test orders of the modes and associated parameters by exploiting the local statistics of the input video sequence. The proposed two methods can reduce substantially the computation requirements as compared to the full search mode selection method. Based on the proposed motion estimation and mode selection algorithms, we implement an H.263-based video coder and an MPEG-2, compliant video coder for very low bit rate video applications, and high bit rate interlaced video applications, respectively. The resulting video coders achieve excellent tradeoffs among bit rate, quality, and computational complexity. In fact, experimental results show that our video coders outperform the best known video coders in terms of compression performance and encoding speed.

Motion estimation using multiple reference frames is widely used as the basis for recent video coding standards (eg. H.264/AVC) to achieve increased coding efficiency. However, this increases the complexity of the encoding process. In this paper, a new technique for efficient motion estimation is proposed. A combination of multiple reference frame selection and image residue-based mode selection is used to improve motion estimation time. By dynamic selection of an initial reference frame in advance, the number of reference frames to be considered is reduced. In addition, from examination of the residue between the current block and reconstructed blocks in preceding frames, variable block size mode decisions are made. Modified initial motion vector estimation and early stop condition detection are also adopted to speed up the motion estimation procedure. Experimental results compare the performance of the proposed algorithm with a state of the art motion estimation algorithm and demonstrate significantly reduced motion estimation time while maintaining PSNR performance.

The growing popularity of high resolution video and the continuously increasing demands for high quality video on mobile devices are producing stronger needs for more efficient video encoder. Concerning these desires, HEVC, a newest video coding standard, has been developed by a joint team formed by ISO/IEO MPEG and ITU/T VCEG. Its design goal is to achieve a 50% compression gain over its predecessor H.264 with an equal or even higher perceptual video quality. Motion Estimation (ME) being as one of the most critical module in video coding contributes almost 50%-70% of computational complexity in the video encoder. This high consumption of the computational resources puts a limit on the performance of encoders, especially for full HD or ultra HD videos, in terms of coding speed, bit-rate and video quality. Thus the major part of this work concentrates on the computational complexity reduction and improvement of timing performance of motion estimation algorithms for HEVC standard. First, a new strategy to calculate the SAD (Sum of Absolute Difference) for motion estimation is designed based on the statistics on property of pixel data of video sequences. This statistics demonstrates the size relationship between the sum of two sets of pixels has a determined connection with the distribution of the size relationship between individual pixels from the two sets. Taking the advantage of this observation, only a small proportion of pixels is necessary to be involved in the SAD calculation. Simulations show that the amount of computations required in the full search algorithm is reduced by about 58% on average and up to 70% in the best case. Secondly, from the scope of parallelization an enhanced TZ search for HEVC is proposed using novel schemes of multiple MVPs (motion vector predictor) and shared MVP. Specifically, resorting to multiple MVPs the initial search process is performed in parallel at multiple search centers, and the ME processing engine for PUs within one CU are parallelized based on the MVP sharing scheme on CU (coding unit) level. Moreover, the SAD module for ME engine is also parallelly implemented for PU size of 32×32. Experiments indicate it achieves an appreciable improvement on the throughput and coding efficiency of the HEVC video encoder. In addition, the other part of this thesis is contributed to the VLSI architecture design for finding the first W maximum/minimum values targeting towards high speed and low hardware cost. The architecture based on the novel bit-wise AND scheme has only half of the area of the best reference solution and its critical path delay is comparable with other implementations. While the FPCG (full parallel comparison grid) architecture, which utilizes the optimized comparator-based structure, achieves 3.6 times faster on average on the speed and even 5.2 times faster at best comparing with the reference architectures. Finally the architecture using the partial sorting strategy reaches a good balance on the timing performance and area, which has a slightly lower or comparable speed with FPCG architecture and a acceptable hardware cost

This paper presents a method to adopt phase correlation for blurred frames. Phase correlation is a strong motion estimation tool which is highly robust against noise, However, phase correlation is not robust against blur degradation in frames. In scenarios where only one of the frames is blurred or when the two frames have different blur extents, phase correlation does not achieve an accurate estimation of the motion vector. We also show that the number of motion vectors estimated by the block-matching technique is reduced in such scenarios, therefore, a technique to estimate the motion in such scenarios is strongly needed. We investigate the effects of blurring on motion estimation and show the effectiveness of our algorithm for motion estimation in blurred frames and compare it against existing methods.

The rate-constrained (R-D) motion estimation techniques have been presented to improve the conventional block-matching algorithm by using a joint rate and distortion criterion. This paper presents two motion estimation algorithms using Kalman filter to further enhance the performance of the conventional R-D motion estimation at a relative low computational cost. The Kalman filter exploits the correlation of block motion to achieve higher precision of motion estimation and compensation. In the first algorithm, the Kalman filter is utilized as a postprocessing to raise the motion compensation accuracy of the conventional R-D motion estimation. In the second algorithm, the Kalman filter is embedded into the optimization process of R-D motion estimation by defining a new R-D criterion. It further improves the rate-distortion performance significantly.

In modern world more image and video compression technologies improving gradually. But major barrier of the new technologies concepts are repeated from authors. Because authors can’t find in depth of the papers from various technologies. So Survey of Video Compression Techniques very helpful for these types problems in video compression areas. Video compression techniques such as DCT coding, Quantization, Entropy coding, Motion estimation are widely used in video compression techniques. The main focus of this paper is to analyse video compression techniques required for video processing especially to discover how much amount of data to compressed, which techniques is faster and visual quality better and so on. We evaluate the video compression techniques for finding compression ratio in terms of performance, speed and accuracy. We also compare video compression techniques with conventional methods.

This work addresses the problem of rigid motion estimation and 3D reconstruction in vision systems where it is possible to recover the incident direction from image points. Such systems include pinhole cameras and catadioptric cameras. Given two images of the same scene acquired from two positions, the transformation is estimated by means of an iterative process. The estimation process aims at having corresponding incident rays intersecting at the same 3D point. Geometrical relationships are derived to support the estimation method. Experiments with real images are presented.

H.264/AVC, the latest video coding standard, adopts rate-distortion optimization (RDO) technique to obtain the best intra- and inter-prediction, while maximizing visual quality and minimizing the required bitrate. However, the full RD cost calculation for all intra-prediction modes, the exhaustive searches for finding optimal motion vectors for all block sizes, and the multiple references frame procedure considerably increase its computational complexity with the allowed number of prediction modes. The authors have previously proposed an approach for both inter- and intra-mode decisions that takes into account the several effective parameters, image content type, quantization parameter, correlation of motion vectors and motion details of video objects. In order to reduce the complexity, here we propose a new two-step adaptive search range decision by developing a combination of previous algorithm. The first step of our adaptive search range decision determines the search range for current MB according to the motion vectors of the previous MBs. In the second step, the motion vector obtained in the current frame is used to adaptively searching the window size of the previous reference frame for multi-frame motion estimation. As such, only a subset of inter- and intra-modes is chosen for RDO calculation. Experimental results show that the proposed algorithm reduces the total encoding cost with negligible loss in PSNR and a slightly increase in the required bitrate, when compared to our previous algorithm and other algorithms reported in the literature.

Motion estimation is an important component in video compression schemes, which impacts the encoder performance and the quality of the reconstructed video sequence. Block-based techniques are the most popular among the several motion estimation techniques used in practice due to their simplicity allowing efficient hardware implementations. The quality of motion predicted frame and the speed of motion vector computation are the primary measures of performance of motion estimation schemes. The video compression standards only specify the syntax of the bitstream and not any specifics of the motion estimation techniques. Thus, the knowledge of the workings and performance of commonly used motion estimation schemes would facilitate development of better schemes or choosing a particular one for a given application. We present the results of performance evaluation of combinations of several commonly used block-based motion estimation algorithms and distortion measures.