A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

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.

In this paper, we propose a fast block motion estimation scheme by exploiting the correlations of motion vectors (MV) existing in spatially- and temporally-adjacent as well as hierarchically-related blocks. The basic idea is to use the information obtained from the corresponding block at a coarser resolution level and spatio-temporal neighboring blocks at the same level in order to select a good set of initial MV candidates and then perform further local search to find the best matching MV. In order to further reduce computational complexity, the sign pattern vector (SPV) is defined and used for block matching, as opposed to the pixel intensity values used in the conventional block matching methods. By using the SPV definition, a data block can be presented by a mean and a set of binary sign patterns with a much reduced data set. The block matching motion estimation is then divided into mean matching and binary phase matching. The proposed technique enables a significant reduction in computational complexity compared with the conventional block matching motion estimation (ME) because binary phase matching only involves Boolean logic operations. This scheme also significantly reduces the data transfer time between the frame buffer and motion estimator. Our test results indicate that the performance of the proposed scheme is comparable with the full-search block matching under the same search ranges, however, the proposed scheme has a speed-up factor ranging from 250 to 370 in comparison with full search.

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.

Block matching motion estimation (BMME) plays a very important role in video coding. The performance of BMME greatly affects the quality of the encoded bit stream. In order to reduce the high computational demands of H.264 video encoder, a fast block matching motion estimation algorithm based on temporal and spatial motion correlation is proposed by the deep research of the characteristics of motion estimation. This algorithm well exploits the correlation between video sequence motion activity and macro-block coding pattern and motion vector statistic characteristics to employ different patterns. Experimental results show that the algorithm, without function losing of PNSR, can not only reduce the search complexity of motion estimation and the high computational demands but also raise the real-time performance of encoder.

Block matching motion estimation is a significant issue in image and video super resolution reconstruction. In order to decrease the number of search locations, the amount of computations, and enhance the prediction accuracy, an improved block matching motion estimation algorithm based on adaptive rood pattern search (ARPS) is proposed. Firstly, we use spatial correlation characteristic to predict motion vector (MV) of the current macro-block in the algorithm. Secondly, we consider to eliminate the influence of neighborhood macro-blocks having low relevance to the current macro-block in the prediction process. Finally, we utilize the weighted average MV of two macro-blocks with the smaller matching error in the neighborhood macro-blocks to calculate the predictive MV of current macro-block, instead of using the fixed single macro-block forecast pattern in ARPS. Experimental results show that our algorithm provides the better performance in terms of the computational complexity and the prediction precision in comparison with APRS and other block matching algorithms.

The paper presents a new fractal color image compression method, called Fractal Hierarchical Color Block Coding (FHCBC), which transforms the three-color planes of a color image into a one-component image by extracting correlation among them. It hierarchically divides the three-color planes into homogeneous blocks, and for each such block, the variance of pixels' trichromatic coefficients is within a certain threshold value. Then, each block is represented by its mean value of the pixels' trichromatic coefficient ratios, and just a one-component image is composed and compressed by fractal coding. In the decoding process, the reconstruction is carried out by a fractal decoding algorithm and the associated mean values of trichromatic coefficient ratios. In comparison with the well known three-component Separated Fractal Coding (SFC), the suggested method generates performance gains in running time, high compression ratio and equal level of reconstruction quality.

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.

This paper presents a new method for frame rate up‐conversion using sub‐sampled bilateral motion estimation, motion vector smoothing, simplified bidirectional motion compensated interpolation, and complex motion detection. The proposed method produces improved image quality and requires less computational complexity compared to conventional motion estimation. The average PSNRs of the proposed method for frame rate up‐conversion outperform those of the benchmark frame rate up‐conversion technique by up to 4.39 dB for the test sequences. Furthermore, the motion estimation of the proposed FRUC method reduces the required computational complexity to 12.5% of that required for full‐search block matching motion estimation.

Publication in the conference proceedings of EUSIPCO, Rhodes, Greece, 1998

We present the split-merge displacement estimation algorithm as a bridge between the standard, block matching, displacement estimation technique and the semantic motion estimation techniques. We review this new technique and its applications in video compression and show how it can be used to maintain two different segmentation of the current visual field: a spatial segmentation identifying the objects that are present in the scene and a classification of the visual field into moving areas, that keeps track over time of the relationships between the objects.

The goal of compression techniques is to reducing the size of data and decreasing the communication cost while transferring data. Fractal based coding technique is widely used to compress images files which provides high compression ratio and good image quality. However, like a compression technique, it is still limited because of the difference of the human perceptions between audio and image files, the long time for searching the best possible domain blocks and many comparisons in the encoding process. For those reasons, Fractal Coding had not broadly studied on audio data. Few years ago, Fractal Coding has been extended to apply on the audio data. In this paper, the application of the Fractal Coding on different types of audio files is investigated. Moreover, the effect of block length on the audio quality and compression performance are highlighted since block length is considered the main factor in the Fractal Coding algorithm. A GTZAN dataset is adopted in the evaluation and the experimental results show that there is an inverse relationship between block length and audio quality and proportional relationship between block length and compression ratio and factor. Furthermore, it can be noticed that the Fractal Coding can be compressed any speech and music audio signal directly with acceptable quality, PSNR 39 dB on average with a high compression ratio around 90 % with compression factor around 10 when the block length is 20 samples.

In this paper an adaptive algorithm is proposed to reduce the long time that has been taken in the Fractal Image Compression (FIC) technique. This algorithm worked on reducing the number of matching operations between range and domain blocks by reducing both of the range and domain blocks needed in the matching process, for this purpose, two techniques have been proposed; the first one is called Range Exclusion ( RE ), in this technique variance factor is used to reduce the number of range blocks by excluding ranges of the homogenous or flat regions from the matching process; the second technique is called Reducing the Domain Image Size ( RDIZ ), it is responsible for the reduction of the domain by minimizing the Domain Image Size to 1/16 th instead of 1/4 th of the original image size used in the traditional FIC. This in turn will affect the encoding time, compression ratio and the reconstructed image quality. For getting best results, the two techniques are coupled in one algorithm; the new algorithm is called ( RD-RE ). The tested (256x256) gray images are partitioned into fixed (4x4) blocks and then compressed using visual C++ 6.0 code. The results show that RE technique is faster and gets more compression ratio than the traditional FIC and keeping a high reconstructed images quality while RD-RE is faster and it gets higher compression ratio than RE but with slight loss in the reconstructed image quality.

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.

We present an architectural enhancement to reduce the power consumption of one dimensional full-search block-matching (FSBM) motion estimation. The architecture eliminates unnecessary computations and reduces the power consumption without sacrificing the optimality and high throughput. Both the new and old architectures are compared for power consumption in order to clarify the effectiveness.

Motion estimation is the most computationally intensive component of a video coding algorithm. It could consume as much as 75% of the total processing power of a video codec. Although various international video coding standards have adopted the block-matching approach for motion estimation, a designer can still have the freedom to choose a specific technique to find the motion vectors. Consequently, numerous techniques ranging from full search to simple fast search algorithms have been proposed and some of them have been implemented in VLSI chips. It should be emphasized that one of the most important requirements for an effective motion estimation algorithm is its ability to perform in real-time. The choice of a specific algorithm for implementation, however, depends very much on the intended application as well as the trade-offs between the desired performance and affordable complexity. In this paper, we first discuss what are the essential ingredients for an effective block-matching motion estimation, then briefly, describe what is the status of current technology, and finally, present some new activities in this vital area of research.