Fast disparity estimation algorithm for stereoscopic image sequence coding

The challenge to H.265/HEVC multiview video coding (MVC) is its extremely high computational complexity. This paper presents the development and implementation of a scalable massively parallel fast search algorithm to significantly reduce the computational cost of motion and disparity estimation over the current best available GPU full block matching and sub optimal CPU fast search algorithms by a factor up to 2.4 and 8.4, respectively.

To reduce the heavy computational complexity of uni-directional and bi-directional predictions in multiview video coding (MVC), this paper proposes a multiview video coding algorithm. Uni-directional prediction is optimized. By using the geometry relationship between disparity and motion vectors, an iterative process is preformed. In each process, a new adaptive search range is used to revise motion/disparity vector. At last, optimal motion and disparity vectors are achieved. For bi-directional prediction, TZSearch algorithm is utilized to accelerate prediction. Furthermore, an early termination strategy is devised on the basis of relationships among bi-directional prediction, model error, quantization parameter and temporal level. Experimental results show that computational complexity can be significantly reduced while maintaining video coding performance compared with JMVC full search algorithm.

In this paper, a new rate---distortion (R---D) optimized error-resilient scheme is proposed to improve error resilience for multi-view video transmission over lossy networks. Based on the study on the characteristics of multi-view video coding and the propagating behavior of channel errors, a recursive model to estimate the end-to-end distortion is first developed, in which the channel-induced distortion takes into consideration both motion and disparity compensation prediction. Then, based on the estimated distortion and the derived total bit rate, R---D optimized motion and disparity estimation are employed to find the reliable motion vector and disparity vector to achieve the optimum trade-off between prediction efficiency and error robustness. Finally, R---D optimization is applied again to perform coding mode decision, which explicitly considers intra, inter and inter-view prediction modes. Extensive experimental results demonstrate significant performance gains can be achieved for multi-view video communications against transmission errors.

To reduce the amount of computation of the full search (FS) algorithm for fast motion estimation, we propose a new and fast matching algorithm without degradation of predicted images as in conventional FS. The computational reduction without any degradation in the predicted image comes from fast removal of impossible motion vectors. We obtain faster removal of inappropriate motion vectors using efficient matching units from the localization of complex area in image data. In this paper, we show three properties in block matching of motion estimation. Experimentally, we reduce unnecessary computations by about 30% with our algorithm compared with the conventional fast matching scan algorithm.

In this paper we propose a new technique for refinement of depth maps. The technique exploits a stereoscopic pair of images and full-pixel precision disparity map in order to produce the output disparity map with sub-pixel precision. The technique employs view synthesis for verification of hypotheses on disparity values, which are formed to iteratively improve the disparity map. Results of experiments, presented for still images and video sequences, show that proposed technique increases quality of depth and has low computational cost. Applications of the proposed technique can be found in future real-time 3D video broadcasting systems which require fast and robust disparity estimation algorithm with high precision.

Based on the temporal and interview redundancy of stereoscopic video coding, a fast motion and disparity estimation algorithm is proposed in this paper. Firstly, a credible start point is designed taking use of the view-temporal correlation of neighboring motion and disparity vectors. Secondly, an improved searching strategy is proposed to reduce the computational complexity, in which only 2-pixel refinement is needed around the start point. The proposed algorithm can get both motion vector and disparity vector during one estimation process. The experimental results demonstrate that the proposed algorithm can reduce up to 96% coding time compared with inter-view full search with a negligible quality drop.

Disparity estimation is a key technology for stereo video compression.Considering the disadvantage of the epipolar correction algorithm,a fast disparity estimation algorithm based on the features of disparity vector was proposed.The algorithm analyzed the features of disparity vector in parallel camera and convergent camera systems respectively,and explained how to find the best matching block by a three-step search according to their features.The algorithm was tested in both 640×480 and 1 280×720 resolution sequences.The experimental results show that compared to the original TZSearch algorithm in JMVC,the proposed algorithm can effectively shorten the encoding time and improve coding efficiency without decreasing the image quality and compression efficiency.Because there is not epipolar correction in the proposed algorithm,the disadvantage caused by epipolar correction will not appear.

This paper presents an efficient joint disparity-motion estimation algorithm and a fast motion estimation algorithm in an MPEG-4 compatible stereoscopic sequence codec for 3D broadcasting. In stereoscopic sequence coding, the critical problems to be solved are double bandwidth and computational complexity. However, the previous trials to reduce coding bits have increased computational complexity. In this paper, we try to implement fast algorithms for stereoscopic sequences while maintaining high coding efficiency. Joint disparity-motion estimation can increase coding efficiency and reduce the computational complexity of the stereoscopic codec by using the relationship between the disparity and motion fields. The disparity vectors are estimated by using the left and right motion vectors and the previous disparity vectors in each time frame. In order to obtain more accurate disparity vectors, we employ the spatial prediction process after joint estimation. From joint estimation and spatial prediction, we can obtain the accurately predicted disparity vectors and then increase the coding efficiency from those predicted vectors. Moreover, we propose a fast motion estimation algorithm which utilizes correlation between the motion vectors of the neighboring blocks. We confirmed that the PSNR of the proposed method is 1.0-2.0 dB higher than ones of conventional methods. In addition, the processing time is reduced by almost 1/10.

In this paper, we propose a variable step search fast motion estimation algorithm using local statistics of neighboring motion vectors. Using the degree of correlation between neighboring motion vectors, motion search range of each macroblock is adaptively adjusted to reduce unnecessary search points. Based on the determined search range, motion vector is obtained by variable step search step. Experimental results show that comparing to fast full spiral search, fixed step, and fast variable block size motion estimation algorithms, the motion search points and computing time of proposed algorithm are dramatically reduced with acceptable loss in coding performance.

In this paper, we propose the prediction search algorithm for block matching using the temporal/spatial correlation of the video sequence and the renter-biased property of motion vectors The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(Sum of Absolute Difference) value by the predicted motion vector from the same block of the previous frame and the predictor candidate pint in each search region and the predicted motion vector from the neighbour blocks of the current frame. And the searching process after moving the starting point is processed a adaptive search pattern according to the magnitude of motion vector Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved up to the 0.75dB as depend on the video sequences and improved about 0.05∼0.34dB on an average except the FS (Full Search) algorithm.

This paper proposes a fast motion estimation algorithm for variable block-sizes by utilizing motion vector bottom-up procedure for H.264. The refined motion vectors of adjacent small blocks are merged to predict the motion vectors of larger blocks for reducing the computation. Experimental results show that our proposed method has lower computational complexity than full search, fast full search and fast motion estimation of the H.264 reference software JM93 with slight quality decrease and little bit-rate increase.

For video compression, motion estimation is popularly employed to exploit temporal correlation existing in video sequences. If we employ the full search block matching algorithm for estimating motion vectors, it requires very heavy computational complexity. Although several fast block matching algorithms have been proposed to solve this problem, they sacrifice their reconstructed image quality. In this paper, we derive optimal search patterns for fast block matching motion estimation. By analyzing the block matching algorithm as a function of the block size and the shape, we find optimal search patterns for initial motion estimation. The proposed idea can provide an analytical ground for the current MPEG-2 proposals. In addition, we propose a new fast motion estimation algorithm using adaptive search patterns, considering matching criteria and statistical properties of object displacement. In order to select an appropriate search pattern, we exploit the relationship between the motion vector and the frame difference of each block. By changing the search pattern adaptively, we can improve the motion prediction accuracy, while reducing the required computational complexity compared to other fast block matching algorithms.

This paper proposes a parallel architecture for a successive elimination algorithm (SEA), which is used in block matching motion estimation. SEA effectively eliminates the search points within the search window and thus decreases the number of matching evaluation instances that require very intensive computations compared to the standard full search algorithm (FSA). The proposed architecture for SEA decreases the time to calculate the motion vector by 57 percent compared to FSA. The performance while applying the SEA to several standard video clips has been shown to be same compared to the standard FSA. The proposed architecture uses 16 processing elements accompanied with use of intelligent data arrangement and memory configuration. A technique for reducing external memory accesses has also been developed. The proposed architecture for SEA provides an efficient solution for applications requiring real-time motion estimations. For, it serves to compute motion vectors in less amount of time while requiring almost same power and some increase in area compared to a similar architecture for implementing the full search algorithm. A register-transfer level implementation as well as simulation results on benchmark video clips are presented. Relevant design statistics on area and power for comparing between SEA and FSA implementations are also provided.

Many suboptimal motion vector search algorithms have been proposed because the full search algorithm, which is an optimal method, requires huge computational requirements. These block matching algorithms find motion vectors simply from the center of the search window. But the motion vector of the current block is highly related to those of its neighboring blocks. In this paper we propose an efficient hybrid search algorithm (HBS) that exploits the motion vectors of the neighboring blocks to predict the initial search point. HBS searches for a motion vector from the initial search point using the diamond search algorithm(DS). DS is chosen because it is one of the fastest motion vector search algorithms. Experimental results show that HBS is a faster and more accurate motion vector search method than other suboptimal methods for most test sequences.

A fast and efficient hybrid block-matching motion estimation algorithm is proposed in this paper. The fact that motion characteristics are relatively stable in a GOP (group of pictures) is utilized to reduce the computation burden without quality degradation. For P-frames, a multi-resolution block-matching algorithm is used. At the coarsest level, edge information is used to reduce searching positions before an exhaustive search. At the finer levels, only the several candidates chosen at the upper level are verified. For B-frames, MAE (mean absolute error) is used not only for the matching criteria, but also for classifying the macroblocks with the motion information extracted from P-frames. Concurrently, the search window size is adjusted using the contribution of different motion range to the improvement of the picture quality with motion compensation. When the decoded I-frame and P-frames are used as the reference, experimental results show that reconstructed digital frames have even better quality than the full search algorithm in terms of PSNR, under MPEG-1 coding environment.