Evaluation and comparison of different fast block matching algorithms using motion estimation

In this paper, a new technique called Mean Predictive Block Matching (MPBM) is proposed. This technique is based on adaptive rood pattern search algorithm, in which the size is dynamically determined for each macroblock, based on the mean of two motion vectors of the neighboring macroblocks instead of one as is the case with adaptive rood patter search. The proposed technique attempts to decrease the computation time between the current macroblock and the macroblocks in the reference frame, by stopping the calculation of the sum absolute different between the pixels in the current and the reference frame macroblocks when the current uncompleted sum absolute value is greater than the previous calculated one. Experimental results showed that the proposed technique reduces the search time of the macroblock matching, as well as achieves better resolution for fast block motion estimation in comparison to the standard block matching algorithms.

Emerging video standards addresses mainly content-based functionalities and interactive multimedia. This requires a prior decomposition of sequences into semantically meaningful physical objects. We formulate this problem as one of separating foreground objects from the background based on motion information. The algorithm begins with the motion estimation accomplished using ARPS (adaptive Rood pattern search) algorithm on any two frames of image sequence. A binary object model representing edge features is derived using Canny edge detector and tracked in subsequent frames using motion vectors generated from ARPS algorithm. The object model is updated for each frame to accommodate for complex motions and shape changes. Motion based region merging is performed finally to merge regions with similar motion to extract moving objects. Experimental results demonstrate the performance of our proposed algorithm.

In this paper, Motion Estimation (ME) and Motion Compensation (MC) techniques are used for video compression which eliminates the temporal redundancy of video sequence. For motion estimation, Block Matching (BM) algorithm has been used among the various motion estimation algorithms because of its simplicity and efficiency. In this paper, the performance of block matching algorithm such as Four Step Search (4SS) has been improved using Block Transform Scaling (BTS). In BTS all the varying nature frames of video transform into uniform frames using adaptive filter, which fills the pixel information where pixel value is missing within the frame. The proposed work mainly focuses on the compression of videos which have medical information in it. At the end of this paper the result analysis has been done using the parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Computation Time (CT) and Compression Ratio (CR).

A Fast and Adaptive Search Algorithm Based on Rood Pattern and Gradient Descent

Performance enhanced spatial video compression using global affine frame reconstruction

High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed‐up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed‐up in terms of encoding time and bit rate saving with tolerable peak signal‐to‐noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.

摘要: 图像配准是超分辨率重建中的一个关键问题, 直接影响超分辨率重建图像的质量. 本文在自适应十字搜索(Adaptive rood pattern search, ARPS)块匹配算法的基础上, 根据小波域中各图像之间的相关性, 提出一种分层块匹配算法---基于小波变换的改进的自适应十字模式搜索算法(Improved adaptive rood pattern search algorithm based on wavelet transform, W-IARPS), 该方法在小波变换域完成匹配宏块的搜索, 有效地减少了匹配点的搜索个数, 且配准图像的峰值信噪比相比全搜索算法下降不到0.l dB, 保持了较高的配准精度. 最后采用凸集投影(Projections onto convex sets, POCS)算法对配准后的图像进行超分辨率重建, 取得了较好的视觉效果. 实验结果表明, 该方法具有较高的配准精度和重建效果, 算法稳健可靠. 关键词: 超分辨率 / 块匹配 / 图像重建 / 小波变换 / 凸集投影

In Scalable Video Coding (SVC), motion estimation and inter-layer prediction play an important role in elimination of temporal and spatial redundancies between consecutive layers. This paper evaluates the performance of widely accepted block matching algorithms used in various video compression standards, with emphasis on the performance of the algorithms for a didactic scalable video codec. Many different implementations of Fast Motion Estimation Algorithms have been proposed to reduce motion estimation complexity. The block matching algorithms have been analyzed with emphasis on Peak Signal to Noise Ratio (PSNR) and computations using MATLAB. In addition to the above comparisons, a survey has been done on Spiral Search Motion Estimation Algorithms for Video Coding. A New Modified Spiral Search (NMSS) motion estimation algorithm has been proposed with lower computational complexity. The proposed algorithm achieves 72% reduction in computation with a minimal (<1dB) reduction in PSNR. A brief introduction to the entire flow of video compression H.264/SVC is also presented in this paper.

Block-matching algorithm is the most common technique applied in block-based motion estimation technique. There are several block-matching algorithm based on block-based motion estimation techniques have been developed. Full search (FS), three step search (TSS), new three step search (NTSS), diamond search (DS) and hexagon based search (HS) are the most well known block-matching algorithm. These techniques are applied to video sequences to remove the temporal redundancy for compression purposes and to gauge the motion vector estimation. In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. In order to develop the proposed methods, the baseline techniques are studied to develop the proposed algorithms. This chapter proposes modelling of fast unrestricted hexagon search (FUHS16) and unrestricted hexagon-diamond search (UHDS16) algorithms for motion vector estimation, which is based on the theory and application of block-based motion estimation. Both of these algorithms are designed using 16 × 16 block size. In particular, the motion vector estimation, quality performance, computational complexity, and elapsed processing time are emphasised. These parameters have been used to measure the experimental results. It is the aim of this study that this work provides a common framework with which to evaluate and understand block-based matching motion estimation performance. On the theoretical side, four fundamental issues are explored: (1) division of frame, (2) basic block-based matching, (3) motion vector estimation, and (4) block-matching algorithm development. Various existing block-matching motion estimation algorithms have been analysed to develop the fundamental research. Based on the theoretical and fundamental research analysis the FUHS16 and UHDS16 algorithms using 16 × 16 block-based motion estimation formulations were developed. To improve the UHDS16 algorithm, 8 × 8 block-matching technique has been tested. The 8 × 8 block-matching technique is known as UHDS8. The results show positive improvements. From an application perspective, the UHDS8 algorithm efficiently captured the motion vectors in many video sequences. For example, in video compression, the use of motion vectors on individual macro-blocks optimized the motion vector information. The UHDS8 algorithm also offers improvement in terms of image quality performance, computational complexity and elapsed processing time. Thus, this chapter offers contributions in certain areas such as reducing the mechanism of computational complexity in estimating the motion from the video sequences. In particular, the FUHS16, UHDS16 and UHDS8 algorithms were developed to estimate the motion vectors field in the video sequences. Theoretical analysis block-based matching criteria are adapted to FUHS16, UHDS16 and UHDS8 algorithms, which are based on search points technique. Basically, the proposed of FUHS16, UHDS16 and UHDS8 algorithm produces the best motion vector estimation finding based on the block-based matching criteria. Besides that, the UHDS8 algorithm also improves the image quality performances and the search points in terms of the computational complexity. Overall, the study shows that the UHDS8 algorithm produces better results compared to the FUHS16 and UHDS16 algorithm.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

Image and video coding plays a salient role in numerous multimedia applications. Nowadays, digital video communication is adopted in many applications, for example broadcast services over satellite and terrestrial channels, digital video storage, and more. It is impractical for us to store the full digital video without processing. It is well-known that video compression is a process of diminishing the size of data used to depict digital video images, and it is an amalgamation of spatial image compression and temporal motion compensation. The primary intent of our research is to propose a very potent video compression system. Originally, wavelet decomposition is applied to the I-frame and the resulting coefficients are quantized with the aid of Listless SPECK (LSK) algorithm. Secondly, an adaptive rood search with spatio-temporal correlation technique (ARS-ST) is employed for motion estimation and it finds the displacement of the blocks of P-frame from the I-frame. The proposed video compression technique is evaluated by using different videos and the compression efficiency is computed by determining the peak signal to noise ratio (PSNR) values. Also, the efficacy of the proposed system is compared with existent video compression systems. From the comparative study, it can be seen that our proposed system robustly compress the videos with adequate PSNR measurements.

Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10–15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS-GA method combines the HS with genetic algorithm (GA) to optimize ME. The HS-GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to ∼316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.

Traditionally, peak signal-to-noise ratio (PSNR) has been used to represent the quality of a compressed video sequence. However, PSNR has been found to correlate poorly with subjective quality ratings, particularly at much lower bit rates and frame rates. This paper proposes an objective video quality metric to automatically measure the perceived quality of a stream of video images based on a combined measure of distortion-invisibility, block-fidelity, and content richness fidelity. The proposed method has been tested on CIF and QCIF video sequences compressed at low bit rates and frame rates and it is shown to give significantly better correlations to human perception than peak signal-to-noise ratio (PSNR).

A neural network based motion-estimation technique (for successive video frames) is developed that is applicable to purely translational as well as affine movements. In addition to yielding highly accurate results, a benefit of this technique is the elimination of fractional-pixel interpolation and corresponding search. The technique is formulated in terms of a modified Hopfield neural network. The procedure consists of two stages: estimation of the neural network parameters from the present and previous frames, followed by the estimation of the affinity weights for the candidate macroblocks. The reconstructed macroblock then consists of the sum of the candidate macroblocks weighted by their affinity values as defined. Experiments on translational motion indicate that this technique produces a 5 dB higher peak signal to noise ratio (PSNR) than the full search algorithm, 6 dB higher than the logarithmic block matching algorithms, and 2 dB higher than some of the very recent motion estimation algorithms. Preliminary experiments using the neural network affine algorithm on synthetically sheared images produce an 11 dB higher PSNR than the full search and LBM. Further, due to the neural network's fault tolerant nature and inherent parallelism, potential exists for VLSI implementation as an interconnection of small cells on a single chip.

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.