Adaptive Block Matching Algorithm Research Articles

Video compression and compensation of moving target image based on redundant wavelet theory

The redundant wavelet transform has the translation invariant property, and the motion estimation can achieve better results in the redundant wavelet domain. This paper proposes the redundant discrete wavelet transformation (RDWT) for motion estimation and compensation in video compression and compensation of moving target image, and a fast adaptive block matching motion estimation algorithm based on redundant wavelet transform. This paper also introduces the methods of image domain search, starting point prediction and motion estimation into the redundant wavelet transform, and the study method based on optical flow which can enhance the transmission robustness with low redundancy. The experimental results show that the algorithm can effectively reduce the time required for motion estimation and improve the efficiency of motion estimation, and the subjective quality of the motion estimation is very good. The research results can provide technical support for robot vision system.

Video compression and compensation of moving target image based on redundant wavelet theory

The redundant wavelet transform has the translation invariant property, and the motion estimation can achieve better results in the redundant wavelet domain. This paper proposes the redundant discrete wavelet transformation (RDWT) for motion estimation and compensation in video compression and compensation of moving target image, and a fast adaptive block matching motion estimation algorithm based on redundant wavelet transform. This paper also introduces the methods of image domain search, starting point prediction and motion estimation into the redundant wavelet transform, and the study method based on optical flow which can enhance the transmission robustness with low redundancy. The experimental results show that the algorithm can effectively reduce the time required for motion estimation and improve the efficiency of motion estimation, and the subjective quality of the motion estimation is very good. The research results can provide technical support for robot vision system.

An efficient eye detection and tracking system based on particle swarm optimization and adaptive block-matching search algorithm

The problem of eye detection and tracking in video sequences is very important for a large number of applications ranging from face recognition to gaze tracking. Eye detection and tracking are challenging due to a variety of factors such as eye-blinking, partially closed eyes, and oblique face orientations which tend to significantly limit the efficiency of most eye trackers. In this paper, an efficient eye detection and tracking system is presented to overcome these limitations. The proposed system switches between the particle swarm optimization (PSO) based deformable multiple template matching algorithm and the adaptive block-matching search algorithm to improve the efficiency and robustness of the tracking system. For eye detection, PSO-based deformable multiple template matching is employed to estimate the best candidate of the center of the eyes within an image of the video sequence with the highest accuracy. For eye tracking the block-matching algorithm with adaptive search area is utilized to reduce the computational time required to perform the PSO-based algorithm. Experimental results on the standard VidTIMIT database show that the proposed method outperforms the deformable template matching based methods such as genetic and PSO. Moreover, it achieves better performance compared to model-based methods such as the statistical active appearance model (AAM) method and the edge projections based method in terms of accuracy and computational complexity.

Histogram ordering model-based fast motion estimation

This study proposes two efficient block matching algorithms for fast motion estimation using a histogram ordering model in order to reduce the computational cost of video coding. Since the representative pixels that consist of edge or texture mainly contribute to the block matching criterion, it is important to analyse the details of the current block. To analyse the characteristics of the block with low complexity, the pixel histogram is used for the observation of the block. Based on this pixel histogram for the current block, an optimal block matching order is determined for the lossless fast matching algorithm. In addition, adaptive partial block matching algorithm for lossy fast motion estimation using histogram-based block matching ordering is also presented to further reduce the complexity of block matching. By capturing the representative pixels, block matching distortion is maximised soon enough during the block matching. For this reason, partial block matching is available instead of full block matching, which is also known as a full search algorithm. Our experimental results show that the proposed algorithm not only reduces the computational complexity of block matching criterion, but also tends to maintain the image quality when compared to the conventional fast matching algorithms.

Image mosaic with relaxed motion

We propose a novel method to stitch images with relatively large roll or pitch called relaxed motion, which defies most existing mosaic algorithms. Our approach adopts a multi-resolution strategy, which combines the merits of both feature-based and intensity-based methods. The main contribution is a robust motion estimation procedure which integrates an adaptive multi-scale block matching algorithm called TV-BMA, a low contrast filter and a RANSAC motion rectification to jointly refine motion and feature matches. Based on TV − L1 model, the proposed TV-BMA works on the coarsest layer to find a robust initial displacement field as the initial motion for source images. This motion estimation method can generate robust correspondences for further processing. In the subsequent camera calibration step, we also present two stable methods to estimate the camera matrix. To estimate the focal length, we combine the golden section search and the simplex method based on the angle invariance of feature vectors; to estimate the rotation matrix, we introduce a subspace trust region method, which matches features based on the rotation invariance. Extensive experiments show that our approach leads to improved accuracy and robustness for stitching images with relaxed motion.

Object Detection by Gaussian Mixture Model and Shape Adaptive Bidirectional Block Matching Algorithm

We proposed a method to improve moving object detection capability of Gaussian Mixture Model by suggesting shape adaptive bidirectional block matching algorithm. This method achieves more accurate detection and tracking performance at various motion types such as slow, fast, and bimodal motions than that of Gaussian Mixture Model. Experimental results showed that the proposed method outperformed the conventional methods.

Digital Magnification of 3D Imaging Using an Intermediate-View Reconstruction Based on an Adaptive Block Matching Algorithm

In this paper, digital magnification of three-dimensional (3D) imaging, using an ABMA intermediate view reconstruction (IVR) based on an adaptive block matching algorithm is proposed. The number of the elemental images obtained from a one-step pickup process can be computationally increased through the use of the proposed IVR without the need for mechanical movement and a long multistep pickup process. To show the feasibility of the proposed magnified 3D imaging system, using test images of a car, bottle, phone, and watch, it was shown that the proposed ABMA algorithm improves the peak signal to noise ratio of reconstructed intermediate images by as much as 5.16 dB on average in comparison with a conventional block and feature-based algorithm.

A statistical adaptive block-matching motion estimation

We address the problem of motion estimation (ME) in digital video sequences and propose a new fast, adaptive, and efficient block-matching algorithm. Higher quality and efficiency are achieved using a statistical model for the motion vectors. This model introduces adaptation in the search window, drastically reducing the number of positions where correlation-type computation is performed. The efficiency is further improved by progressively undersampling the macroblock. Patterns for undersampling are proposed to obtain the maximum benefit from single instruction multiple data (SIMD) instructions. In contrast with existing motion-estimation techniques, search strategy and subsampled patterns are closely linked. This shows that a good search strategy is much more important than blindly reducing the number of pixels considered for the matching pattern. We describe an implementation of the proposed matching strategy that exploits the very long instruction word (VLIW) and SIMD technology available in the new Itanium processor family. Results show that the proposed algorithm adapts easily to the evolution of the scene avoiding annoying quality drops that can be observed with other deterministic algorithms. The total number of operations required by the proposed method is inferior to those required by traditional approaches.

A distributed adaptive block matching algorithm: Dis-ABMA

In the context of estimation for video sequences processing, variable block size algorithms, like the Adaptive Block Matching Algorithm (ABMA), have been proposed to match better objects in motion compared to the classical BMA. However, the variable block size grid derivation and the related estimation relies on a regularization process which implies heavy iterative and inter-dependent computations. Though the parallelization of the BMA is straightforward, the ABMA needs a deeper analysis before its implementation in a distributed environment: this is the goal of this paper. We first designed a modelization of the estimation of ABMA. This model call lead to several different distributed versions. A specific distributed model, with one master and several slaves, is then described. An implementation of this model has been realized and experimentations demonstrate a linear speedup with respect to the number of processors. (C) 2001 Elsevier Science B.V. All rights reserved.

Reconstruction 3D robuste du visage: approche duale « mouvement-structure »

An original approach for three-dimensional reconstruction and animation of human face with a monocular and un-calibrated acquisition system is presented. This method is based on the classical analysis-synthesis scheme. A three-dimensional reconstruction of shape and movement is performed with a combination of two algorithms using the well-known “Zfrom motion” principle. Therefore a pre-processing step is needed to first detect the human face on videoconference sequence. Then, a set of points is tracked with a new hybrid and adaptive block-matching algorithm using the Delaunay tessellation. A local algebraic projective invariant family performs the 3D movement classification to satisfy the 3D synthesis assumptions. Experimental results on synthetic and noisy data set validate the entire approach.

Adaptive block matching algorithm for video compression

A simple adaptive block matching algorithm for video coding is presented. In the proposed algorithm, a matching criterion called bit-plane matching is proposed to sort out a number of blocks with unlikely directive information to the test block from the search window. An adaptive searching method is developed to further improve the computational effectiveness of the system. In this searching method the search origin, search range and pixel subsampling ratio can be adjusted according to the motion content of the block. Simulation results show that the proposed algorithm achieves similar MSE performance to the full-search method and about 92–95% savings in computation.

Two block-based motion compensation methods for video coding

Two new motion compensation methods for video coding are presented. The first method named motion-partitioned adaptive block matching algorithm (MPA-BMA) uses motion boundary information as the sub-dividing or merging criteria to form variable-size matching blocks. The second method named predictive block matching algorithm (PBMA) eliminates the need of sending motion data by predicting motion vectors from previously transmitted pixels. The performance evaluation of both algorithms are given in the experiments.

Adaptive block matching motion estimation algorithm for video coding

A simple adaptive block matching algorithm is proposed for motion estimation. The proposed method exploits the motion correlation of spatially neighbouring blocks to determine the search origin and adjusts the search range according to different motion content of the block. Simulation results show that the proposed algorithm achieves nearly the same MSE performance as the full-search method but requires ~80% less computation.

Adaptive motion estimation based on texture analysis

Texture classification applied to the frame difference signals can be used to design adaptive algorithms for a variety of video applications. In this paper an adaptive block matching motion estimation algorithm based on the interframe texture analysis is presented. The algorithm adaptively changes the size and shape of the search window of each block depending on the values of textural features. The chosen features are extracted from the temporal difference histogram and provide information about the speed of the moving objects and the most likely direction of their motion. Applications of the proposed algorithm to the normal and hierarchical motion estimators are shown. Compared to the known techniques, the presented methods reduce the computational effort by saving unnecessary searches while offering higher performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Backward predictive block matching algorithm for low bit rate video coding

In contrast to the conventional block matching algorithm (BMA) in video coding, a backward predictive BMA which does not need the transmission of the motion vector is investigated. The motion vector is estimated using the previously estimated motion vectors and reconstructed pixels adjacent to a block to be coded. For application to low bit rate video coding, backward/forward adaptive BMA is devised. At 59kbit/s the proposed scheme provides 0.2–0.5dB improvement in peak signal to noise ratio (PSNR) over the conventional BMA.