Partial Distortion Elimination Research Articles

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm.

FAST FRACTAL IMAGE ENCODER DESIGN

Fractal theory has been widely applied in the filed of image compression due to the advantage of resolution independence, fast decoding, and high compression ratio. However, it has a fatal shortcoming of intolerant encoding time because that every range block is need to find its corresponding best matched domain block in the full image. Therefore, it has not been widely applied as other coding schemes in the field of image compression. In this paper, an algorithm is proposed to improve this time-consuming encoding drawback by the adaptive searching window, partial distortion elimination and characteristic exclusion algorithms. Proposed can efficient decrease the encoding time significantly. In addition, the compression ratio is also raised due to the reduced searching window. Conventional fractal encoding for a 512 by 512 image need search 247009 domain blocks for every range block. Experimental results show that our proposed method only search 120 domain blocks which is only 0.04858% compared to conventional fractal encoder for every range block to encode Lena 512 by 512 8-bit gray image at the bit rate of 0.2706 bits per pixel (bpp) while maintaining almost the same decoded quality as conventional fractal encoder does. This paper contributes to the research of encoder of fast image communication system.

Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search.

Digital Image Stabilization Based on Harmony Filter

A digital image stabilization algorithm based on a harmony filter is presented in this paper. The corner points are extracted from the reference image. A new motion estimation method based on diamond search (DS) and partial distortion elimination (PDE) algorithm is proposed in the feature block matching. The global motion parameters are obtained based on the affine transformation model and the least square solution. Then a new filter algorithm named harmony filter is adopted to smooth the global motion by separating the active scanning movement from random jitter of camera. Finally, the original jitter sequences are compensated by the correction vectors. Experimental results show that our method can eliminate the high frequency tremble, keep active motion of camera and realize real-time image stabilization.

FVQEOPT: Fast Vector Quantization Encoding with Orthogonal Polynomials Transform

This paper deals with a design of new vector quantization algorithm for coding of color images in the Transform domain. In order to speed up the design issue, the feature of transform coding is combined with vector quantization. The transformed training set is obtained with the proposed integer Orthogonal Polynomials transform with reduced computational complexity. The proposed method then generates a single codebook for all the three color components, utilizing the inter-correlation property of the individual color plane as well as interactions among the color planes with the proposed transformation. In the codebook generation phase of the proposed vector quantization encoding, binary tree method is used to achieve considerable saving in codebook construction time. The vector encoding phase uses binary search and partial distortion elimination to further reduce the encoding time for finding closest codeword of an input vector. The experimental result shows that the proposed algorithm greatly reduces the encoding time. The proposed algorithm is also compared with existing standard LBG algorithm.

Statistical digital image stabilization

In this paper, an efficient algorithm for the statistical digital Image stabilization (SDIS) is proposed to reduce the computational cost of block matching algorithm, for local motion estimation. This method is based on calculation of statistical functions, mean and variance of pixels in each block; four blocks are used in each frame. By using the statistical functions, the best block is selected then the local motion vector (LMV) of selected block is estimated by using full search algorithm. LMV of selected block is used as global motion vector (GMV) and it is used to stabilize current frame. Full search algorithm is used for motion estimation, but instead of searching points in a block for full search, it is used in the partial distortion elimination method to terminate the improper candidate blocks and reduce computation for block matching. Key words: Digital image stabilization, motion estimation, block matching algorithm, global motion vector, statistical functions, partial distortion elimination.

Hybrid parallel motion estimation architecture based on fast top-winners search algorithm

In this paper, a hybrid parallel motion estimation architecture based on the fast top-winners algorithm is proposed. In the first instance, the fast top-winners search algorithm is discussed based on the pel-subsampling technique to reduce the computational amount of the sum of absolute difference (SAD). Moreover, the four-parallel spiral scanning (4PSP) with the partial distortion elimination (PDE) mechanism is also utilized to early terminate the unnecessary SAD. Therefore, the proposed fast algorithm can not only avoid trapping into the problem of the local minimum but also save the computational operations with a little performance degradation. According to our proposed algorithm, the 4×4 processing element (PE) array and the dual mode SAD tree are proposed to efficiently perform SAD and Sub-SAD which is accumulated based on the pel-subsampling. For the sake of reducing the system memory bandwidth and decreasing the frequency of the memory access, the local memory configuration and the novel memory interleaving organization are proposed to arrange the current data and reference pixels easily, to access the image pixels efficiently, and to achieve the Level C (Lv. C) data reuse scheme.

A content adaptive fast PDE algorithm for motion estimation based on matching error prediction

This paper introduces a new fast motion estimation based on estimating a block matching error (i.e., sum of absolute difference (SAD)) between blocks which can eliminate an impossible candidate block much earlier than a conventional partial distortion elimination (PDE) scheme. The basic idea of the proposed scheme is based on predicting the total SAD of a candidate block using its partial SAD. In particular, in order to improve prediction accuracy and computational efficiency, a sub-sample based block matching and a selective pixel-based approaches are employed. In order to evaluate the proposed scheme, several baseline approaches are described and compared. The experimental results show that the proposed algorithm can reduce the computations by about 44% for motion estimation at the cost of 0.0005 dB quality degradation versus the general PDE algorithm.

Fast motion estimation based on search range adjustment and matching point decimation

This study introduces a new fast motion estimation (ME) based on both an adaptive search range adjustment and a matching point decimation. In particular, the authors present a maximum matching error constraint in the matching phase that can eliminate an impossible candidate block much earlier than a conventional partial distortion elimination (PDE) scheme. The constraint is computed during the matching error computation based on sum of absolute difference (SAD) between two blocks. The basic idea of the proposed scheme is based on adjusting a given search range adaptively and early eliminating invalid matching blocks effectively. The adaptive search range adjustment is first performed by analysing the contents of a scene. Next, a maximum partial matching error in reordered sub-blocks of an optimal block is obtained, and it is set as a trigger to eliminate invalid blocks for ME. The main contributions of the proposed scheme are that (i) it can reduce a search range adaptively based on the analysis of scene contents; (ii) it can make an early decision for an impossible candidate before complete SAD computation; (iii) the proposed constraint can reduce the computational cost considerably for SAD calculation; and (iv) the proposed matching ideas can be applied to conventional PDE algorithms without significant changes. In order to evaluate the proposed scheme, several baseline approaches are described and compared. The experimental results show that the proposed algorithm can reduce the computational cost more than 86% for ME at the cost of 0.02 dB quality degradation on against the conventional PDE algorithm.

New sorting-based partial distortion elimination algorithm for fast optimal motion estimation

In this paper, we present a new sorting-based partial distortion elimination (PDE) algorithm for fast optimal motion estimation. By analyzing the contributions to the true sum of absolute differences (SAD), we found that there is a close relationship between the distances from the mean value of the current block and the contributions to the true SAD. By sorting the distances from the pixels to the current block and subtracting them from a mean value, then applying this order to the typical PDE, we can eliminate impossible candidates faster and save substantial computations. Experimental results show that the proposed algorithm reduces computational complexity by about 45% on average compared with the typical PDE.

Improved algorithm on fast multi-resolution motion estimation

In this paper,an improved algorithm on fast multi-resolution motion estimation was proposed,which made use of the multi-resolution property and wavelet matching error characteristic.Based on Partial Distortion Elimination(PDE) algorithm,we improved the speed of motion estimation by improving searching order,matching order and comparison interval.Experimental results show that the proposed algorithm can achieve the same estimate accuracy as Full Search Algorithm(FSA),while the computation complexity is reduced extremely.

Fast partial distortion elimination algorithm based on Hadamard probability model

A fast partial distortion elimination algorithm based on a Hadamard transform and its probability model is presented. Using the Hadamard transform, we obtain local block complexities, and by computing the cumulative distribution function of the local block complexities, we can accurately predict the total matching error without calculation.

A Fast Encoding Algorithm for Vector Quantization Using Difference Pyramid Structure

This paper proposed a fast vector quantization encoding algorithm called difference pyramid search (DPS). According to the formation of the difference pyramid and partial distortion elimination, a rejection test inequality is derived to progressively reject a lot of nonclosest code words as early as possible. Experimental results show that the proposed DPS algorithm outperforms other pyramid-based fast search algorithms, including mean pyramid search, L2-norm pyramid search, and mean-variance pyramid search.

Low-power systolic array processor architecture for FSBM video motion estimation

A novel power-efficient systolic array architecture is proposed for full search block matching (FSBM) motion estimation, where the partial distortion elimination algorithm is used to dynamically switch off the computation of eliminated partial candidate blocks. The RTL-level simulation shows that the proposed architecture can reduce the power consumption of the computation part of the algorithm to about 60% of that of the conventional 2D systolic arrays.

Fast and low bit rate fractal image encoding

Fractal theory has been widely applied in the field of image compression due to its advantages of resolution independence, fast decoding, and high compression ratio. However, it has a fatal shortcoming of intolerant encoding time for every range block to find its corresponding best matched domain block. In this work, an algorithm is proposed to improve this time-consuming encoding drawback by an adaptive searching window, partial distortion elimination (PDE), and characteristic exclusion algorithms. The proposed methods efficiently decrease the encoding time. In addition, the compression ratio is also raised due to the reduced searching window. While conventional full search fractal encoding to compress a 512×512 image needs to search 247,009 domain blocks for every range block, our experimental results show that our proposed method only needs to search 122 domain blocks, which is only 0.04939% compared to a conventional fractal encoder for every range block to encode a Lena 512×512 8-bit gray image at a bit rate of 0.2706 bits per pixel (bpp) while maintaining almost the same decoded quality in visual evaluation. In addition, the visual decoded quality of the proposed method is better than the most widely used JPEG compressor.

A Partial Norm Based Early Rejection Algorithm for Fast Motion Estimation

Recently, many algorithms have been proposed for fast full search motion estimation. Among them, successive elimination algorithm (SEA) and its modified algorithms significantly speed up the performance of the full search algorithm. By introducing the inequality equation between the norm and the mean absolute difference (MAD) of two matching blocks, the SEA can successively eliminate invalid candidate blocks without any loss in estimation accuracy. In this paper, we propose a partial norm based early rejection algorithm (PNERA) for fast block motion estimation. The proposed algorithm employs the sum of partial norms from several subblocks of the block. Applying the sum of partial norms to the inequality equation, we can significantly reduce the computational complexity of the full search algorithm. In an attempt to reduce the computational load further, the modified algorithms using partial norm distortion elimination (PNDE) and subsampling methods are also proposed. Experimental results show that the proposed algorithm is about 4 to 9 times faster than the original exhaustive full search, and is about 3 to 4 times faster than the SEA.

New sorting-based lossless motion estimation algorithms and a partial distortion elimination performance analysis

In video encoding, block motion estimation represents a CPU-intensive task. For this reason, many fast algorithms have been developed to improve searching and matching phases. A milestone within the lossless approach is partial distortion elimination (PDE/SpiralPDE) in which distortion is the difference between the block to be coded and the candidate prediction block. In this paper, (i) we analyze distortion behavior from local information using the Taylor series expansion and show that our general analysis includes other previous similar approaches. (ii) Then, we propose two full-search (lossless), fast-matching, block motion estimation algorithms, based on the PDE idea. The proposed algorithms, called fast full search with sorting by distortion (FFSSD) and fast full search with sorting by gradient (FFSSG), sort the contributions to distortion and the gradient values, respectively, in order to quickly discard invalid blocks. Experimental results show that the proposed algorithms outperform other existing full search algorithms, reducing by up to 20% the total CPU encoding time (with respect to SpiralPDE), while the computation strictly required by the motion estimation is reduced by about 30%. (iii) Finally, we experimentally find an operational lower bound (based on standard test sequences) for the average number of checked pixels in the PDE approach, which measures the performance of the searching and matching phases. In particular, SpiralPDE achieves performances very close to the searching phase bound, while there is still a remarkable margin on the matching phase. We then show that our algorithms, aimed at improving the performances of the matching phase, achieve interesting results, significantly approaching this margin.

Efficient multilevel successive elimination algorithms for block matching motion estimation

The authors present fast algorithms to reduce the computations of block matching algorithms, for motion estimation in video coding. Efficient multilevel successive elimination algorithms are based on the multilevel successive elimination. Efficient multilevel successive elimination algorithms consist of four algorithms. The first algorithm is given by the sum of absolute difference between the sum norms of sub-blocks in a multilevel successive elimination algorithm (MSEA) using the partial distortion elimination technique. By using the first algorithm, computations of MSEA can be reduced further. In the second algorithm, the sum of absolute difference (SAD) is calculated adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in the SAD calculation can occur early, therefore the computations of MSEA can be reduced. The second algorithm is useful not only with MSEA, but also with all kinds of block matching algorithms. In the third algorithm, the elimination level of the MSEA can be estimated. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is, first of all, to search the motion vector over the half sampled search points. At the second search, the authors search the unsampled search points around the tested search points where the motion vector may exist from the first search results. The motion estimation accuracy of the fourth algorithm is nearly 100% and the computations can be greatly reduced.

Search strategy for partial distortion elimination in motion estimation

A new search strategy for partial distortion elimination (PDE) in motion estimation is proposed, which allows the minimum matching error to be detected faster such that impossible candidates can be rejected sooner and the remaining computation involved in matching criterion can be reduced remarkably. Simulation results show that the new algorithm has a significant computational speed-up without introducing any loss in predicted image when compared to conventional PDE.

Acceleration of Fractal Image Compression by Fast Nearest-Neighbor Search

In this paper we investigate the effect of fast nearest neighbor search method on acceleration of fractal image compression. First we follow the Saupe1 encoding step of fractal image compression that uses the multi-dimensional nearest neighbor search in a projected space. Then we investigate performance of a method for finding the nearest vector called partial distortion elimination. We also propose some supplementary accelerating concepts. For all inspected methods the impact of the tolerance criterion for mean square error of block matching is examined.