Conventional Block Matching Algorithms Research Articles

A local multi-block with various constraints approach for stereo vision in StereoPi

The conventional local block matching algorithms are fast but have an accuracy issue, making them unsuitable to be implemented on real-time applications. This paper studies a local block matching algorithm with different cost computations, combining a cost aggregation with various blocks' shape and sizes, using the computer and StereoPi. Compared to other local block matching algorithms, the Ranking-Gradient-Similarity Multi-Block (RGSMB) algorithm can extract information with a higher cost uniqueness within the predefined local window. Using KITTI 2015 as input data, RGSMB has better accuracy than other local block matching approaches. It is at least 5% better than Census Transform (CT), 23% better than Ranking Transform (RT), and 62% better than Sum of Absolute Differences (SAD) in terms of accuracy. Furthermore, it can achieve lower computational costs on the StereoPi, a low-cost computer vision embedded system. The RGSMB can achieve 2.5 fps with the right image resolution and parameter settings.

Block Matching Algorithm for Motion Estimation using Previous Motion Vector Pattern

motion estimation is major concern in developing video coding applications. Among various available motion estimation techniques block matching is most favorites because of its effectiveness and simplicity for hardware and software both. In block based motion estimation techniques, computation is reduced by limiting the number of candidate search points within the search window or by implementing effective search criterion for picking the correct block. Previous pattern based motion estimation algorithm has been suggested in literature which controls the number of search points drastically and hence reduce the computation cost. In this manuscript, a new block based searching method is proposed that uses the motion vector for the just before calculated block as the motion vector for the current block and directly map the block in referenced frame accordingly. Experimental results show that an increase up to 15% in terms of peak signal to noise ratio (PSNR) has been achieved than conventional block matching algorithm with almost same execution time. Further, in terms of quality/computation ratio where quality and computation has been measured in terms of PSNR and execution time respectively, proposed method has 10-20% gain over existing block based technique.

A Symmetric Motion Estimation Method for Motion-Compensated Frame Interpolation

This paper proposes a new motion-compensated frame interpolation (MCFI) method. The proposed method utilizes a symmetric motion estimation (SME) method, which is a new pixel-wise motion estimation method for intermediate frame interpolation. By using an adaptive search range for the motion estimation, the proposed method can obtain a more reliable motion vector for each pixel than previous MCFI methods that use a conventional block matching algorithm (BMA). In addition, we propose a combined method of the SME and BMA to reduce the computation time of the pixel-wise motion estimation method. The experimental results show that the proposed method outperforms other MCFI methods in terms of generating objectively and subjectively better interpolated frames.

Motion Estimation Based on Adaptive Kalman Filter and Its Application in Video Coding

Most of the traditional block-matching algorithms for motion estimation (ME) can only yield local optimal motion vectors (MVs). In this paper, the autoregressive moving average process (ARMA) model is selected to formulate the correlation of neighboring blocks in a frame, and the adaptive Kalman filtering algorithm is applied to refine the MVs. The horizontal and vertical ARMA models are constructed to utilize the filtering algorithm twice to get a better performance. Our method can also be extended to realize disparity estimation (DE) in order to apply it in a multi-view video coding (MVC) system. The experiment results show the effectiveness of our method to improve the accuracy of conventional fast block matching algorithms.

Analysis and complexity reduction of multiple reference frames motion estimation in H.264/AVC

In the new video coding standard H.264/AVC, motion estimation (ME) is allowed to search multiple reference frames. Therefore, the required computation is highly increased, and it is in proportion to the number of searched reference frames. However, the reduction in prediction residues is mostly dependent on the nature of sequences, not on the number of searched frames. Sometimes the prediction residues can be greatly reduced, but frequently a lot of computation is wasted without achieving any better coding performance. In this paper, we propose a context-based adaptive method to speed up the multiple reference frames ME. Statistical analysis is first applied to the available information for each macroblock (MB) after intra-prediction and inter-prediction from the previous frame. Context-based adaptive criteria are then derived to determine whether it is necessary to search more reference frames. The reference frame selection criteria are related to selected MB modes, inter-prediction residues, intra-prediction residues, motion vectors of subpartitioned blocks, and quantization parameters. Many available standard video sequences are tested as examples. The simulation results show that the proposed algorithm can maintain competitively the same video quality as exhaustive search of multiple reference frames. Meanwhile, 76 %-96 % of computation for searching unnecessary reference frames can be avoided. Moreover, our fast reference frame selection is orthogonal to conventional fast block matching algorithms, and they can be easily combined to achieve further efficient implementations.

Kalman filtering based rate-constrained motion estimation for very low bit rate video coding

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.

Rate-constrained motion estimation using Kalman filter

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 a new motion estimation algorithm using Kalman filter to improve the performance of the conventional R–D motion estimation at a relatively low computational cost. The Kalman filter exploits the correlation of block motion to achieve higher precision of motion estimation and compensation. In the proposed algorithm, the Kalman filter is embedded into the optimization process of R–D motion estimation by defining a new R–D criterion. Simulation results show that the new algorithm improves the rate–distortion performance significantly.

실시간 비디오 통신에 적합한 새로운 이진 연산 움직임 추정 알고리즘에 관한 연구

The motion estimation algorithm based block matching is a widely used in the international standards related to video compression, such as the MPEG series and H.26x series. Full search algorithm(FA) ones of this block matching algorithms is usually impractical because of the large number of computations required for large search region. Fast search algorithms and conventional binary block matching algorithms reduce computational complexity and data processing time but this algorithms have disadvantages that is less performance than full search algorithm. This paper presents new Boolean matching algorithm, called BCBM(Bit Converted Boolean Matching). Proposed algorithm has performance closed to the FA by Boolean only block matching that may be very efficiently implemented in hardware for real time video communication. Simulation results show that the PSNR of the proposed algorithm is about 0.08㏈ loss than FA but is about 0.96∼2.02㏈ gain than fast search algorithm and conventional Boolean matching algorithm.

Computation-aware scheme for software-based block motion estimation

Many fast block-matching algorithms (BMAs) reduce the computational complexity of motion estimation by sophisticatedly inspecting a subset of checking points, and stop only when all those checking points have been examined. This means that the searching process for each current block cannot be interrupted, even when it is performed in a software-based computation environment. Our main goal is to allow the searching process to stop once a specified amount of computation has been performed. A novel computation-aware scheme is proposed, which first dynamically determines the target amount of computation power allocated to a frame, and then allocates this to each block in a computation-distortion-optimized manner. We propose a rate-control-like procedure and a predicted computation-distortion benefit heuristic to realize this scheme. Conventional BMAs, such as full-search block matching, three-step search, new three-step search, four-step search, and diamond search, can be transformed into their corresponding computation-aware BMA versions. In our simulations, the resulting computation-aware BMAs not only exhibit higher efficiency than conventional BMAs, but also allow the motion estimation to terminate after any specified amount of computation has been performed (in units of checking points).

Subblock matching-based conditional motion estimation with automatic threshold selection for video compression

We propose a block-based conditional motion estimation scheme for video compression. The blocks in a frame are classified as active or inactive based on a threshold and only the active blocks are subjected to motion estimation. The threshold value is decided automatically through an iterative algorithm. The motion vectors of the boundary active blocks are estimated using a subblock matching-based scheme. Unlike existing variable size block-matching algorithms, the proposed motion estimation scheme preserves the basic framework of the conventional block-matching algorithm, such as equal number of motion vectors and one-pass processing. A small amount of side information needs to be transmitted for the boundary blocks to indicate the displacements of the their subblocks. The proposed scheme provides a better tradeoff between the complexity and the rate-distortion performance of the encoder than other existing schemes and better subjective quality of the motion-compensated frames than the conventional motion compensation.

Fast motion estimation for shape coding in MPEG-4

Effective shape coding in MPEG-4 requires a motion estimation (ME) procedure, which is a great burden for real-time encoders. This paper deals with fast ME for MPEG-4 shape coding. The proposed algorithm utilizes inherent properties of shape coding, i.e., coding based on motion vector predictor for shape (MVPS) and motion vector (MV) correlation between neighboring binary alpha blocks (BABs). By properly using these properties, the proposed algorithm can reduce a processing speed. In addition, the proposed algorithm can be combined with any conventional fast block matching algorithm (BMA) being used in texture ME and a bit-packing technique based on binary feature of shape information. Simulation results show that the proposed algorithm can reduce the computational complexity of ME for shape coding to 68.3% even in the worst case. Also, by simply combining the proposed algorithm with a fast BMA and a bit-packing technique, we can achieve the complexity suitable for real-time software implementation of MPEG-4 shape encoding.

Multiresolution Video Coding Based on Kalman Filtering Motion Estimation

Motion estimation plays an important role for the coding of video signals in the framework of multiresolution. The conventional block-matching algorithms achieve only integer pixel accuracy, if interpolation is not performed. In this paper, a multiresolution video coding system based on Kalman filtering motion estimation is presented. The Kalman filter-based motion estimation employs the predicted motion and measured motion to obtain the best estimate of motion vector at the lowest resolution subimage. The motion vector is used as the initial estimate of other subimages and then refined with the conventional block-matching algorithm. The multiresolution motion-compensated difference of each subband is encoded by lattice vector quantization according to their statistical properties, respectively. Simulation results show that the proposed coding system not only improves the performance but also reduces the error propagation significantly when compared to the multiresolution video coding system based on conventional block-matching algorithms.

Hybrid algorithm for estimating true motion fields

A hybrid algorithm is proposed for true motion field estimation. This algorithm consists of three steps: block-based initial motion estimation, image segmentation, and wrong-motion-vector detection and correction based on objects. The hierarchical block-matching algorithms are improved for the initial motion estimation. The improved algorithm uses an adaptive technique to propagate motion vectors between hierarchical levels. It produces accurate motion field everywhere, except in the areas of motion occlusion. In order to correct wrong motion vectors in the areas of motion occlusion, the current image is segmented into objects and an object-based method is proposed to process the estimated motion fields. With the object-based method, wrong-motion vectors are detected by approximating the estimated motion field in each object with a motion model, and are corrected using an object-adaptive interpolator. Experimental results show that the improved hierarchical block-matching algorithm outperforms the conventional hierarchical block-matching algorithms. The proposed algorithm results in dense motion fields that are smooth within every object, discontinuous between objects of different motion, and very close to the true motion fields.

Rate-distortion-constrained subband video coding

This paper introduces a subband video coding algorithm for operation over a continuum of rates from very low to very high. The key elements of the system are statistical rate-distortion-constrained motion estimation and compensation, multistage residual quantization, high order statistical modeling, and arithmetic coding. The method is unique in that it provides an improved mechanism for dynamic spatial and temporal coding. Motion vectors are determined in a nontraditional way, using a rate-distortion cost criterion. This results in a smoother and more consistent motion field, relative to that produced by conventional block matching algorithms. Control over the system computational complexity and performance may be exercised easily.

Variable grid size motion estimation using spatial transformations

The conventional BMA (block matching algorithm), which is the most popular video coding scheme assumes that a moving object in a scene undergoes only translational movements. To overcome this problem, general approaches to block matching motion estimation of the real objects by deforming the corresponding blocks in the picture have been proposed. However, conventional spatial transformation based motion estimation algorithms are not practical because of their heavy computational loads. We propose a variable grid size motion estimation method which is more efficient than conventional spatial transformation based methods and gives a better PSNR performance than the conventional BMA.

Hilbert scanning search algorithm for motion estimation

Block-matching algorithms, such as TSS and DSWA/IS, are widely used for motion estimation in low-bit-rate video coding. The assumption behind these algorithms is that when the matching block moves away from the optimal block, the difference between them increases monotonically. Unfortunately, this assumption is often invalid, and therefore leads to a high possibility for the result to be trapped to local minima. In this research, we propose a new multiple-candidate search scheme, Hilbert scanning search algorithm (HSSA), in which the assumption of global monotonicity is not necessary and the local monotonicity can be effectively explored with binary search around each candidate. In HSSA, the number of initial candidates and a threshold to control the selection of candidates from one stage to the next can be adjusted to meet the required search accuracy and/or speed. With properly chosen parameters, the HSSA converges to their optimal results faster and with better accuracy than the conventional block-matching algorithms.

Edge-based motion estimation and region-based contour tracing for very low bit rate video coding

A new algorithm for image sequence coding with a very low bit rate and high picture quality is proposed. The proposed cod- ing system is composed of interframe and intraframe coding. In the interframe coding, an edge-based motion estimation algorithm is used to find the motion vector of a moving block instead of using the conventional block-matching algorithm. The edge-based method re- duces the entropy of the estimated motion vectors within a frame. Since the motion vectors are usually encoded by entropy coding, bit rate is also reduced. In the intraframe coding, a region-based algo- rithm is used to trace the contour of an object and estimate its pa- rameters from a predetermined database of various geometric shapes. It can reduce the blocking effect. By testing several image sequences such as ''Miss America'' and ''Claire'' with 10 frames/s in quarter common intermediate format (QCIF) the proposed coding system has about 1 to 2 dBs higher peak SNRs than the H.263 system. © 1998 SPIE and IS&T. (S1017-9909(98)01401-9)

Rate constrained block matching algorithm for video coding

The rate constrained block matching algorithm (RCBMA) jointly minimizes displaced frame difference (DFD) variance and entropy, or conditional entropy of motion vectors for determining the motion vectors. It is intended for use in low rate video coding applications, where the contribution of the motion vector rate to the overall coding rate might be significant. The DFD variance versus motion vector rate performance of RCBMA employing size K x K blocks is shown to be superior to that of the conventional minimum distortion block matching algorithm (MDBMA) employing size 2 K x 2 K blocks. Constraining of the entropy or conditional entropy of motion vectors in RCBMA results in smoother and more organized motion vector fields than those output by MDBMA. The motion vector rate of RCBMA can also be precisely controlled for each frame by adjusting a single parameter.

Motion compensation based on motion vector interpolation

A modified block matching algorithm (BMA) with motion correlation constraint (MCCBMA) is proposed at first. Then a novel motion compensation algorithm (INTPMC) which computes motion vector for each pixel by interpolating motion vectors is presented. In order to increase interframe prediction performance and decrease the computational complexity, an optimizing algorithm for partial motion vectors is described at last. Experimental results show that the proposed algorithm can improve the prediction performance obviously with a moderately increased complexity compared with the conventional full search block matching algorithm (FSBMA).

Disparity estimation with hierarchical block correlation for stereoscopic image coding

Disparity compensation is widely used to remove the spatial redundancies in stereoscopic image coding. In this paper, we present a disparity estimation algorithm called hierarchical block correlation, which is a hybrid of the hierarchical estimation approach with the correlation matching criterion. To assess the performance of the proposed algorithm, a coding error-based measure is used. The proposed algorithm is compared with two conventional block matching algorithms. The simulation results indicate that the proposed algorithm reduces disparity vector entropy by 10% to 30% and gives comparable or smaller coding errors. Furthermore, the proposed algorithm is superior with regard to the reliability of disparity vectors, and thus can give better image quality for applications such as low-bit-rate coding and image interpolation.