A simple and efficient block motion estimation algorithm based on full-search array architecture

Huang, Shih‐Yu; Tsai, Wan-Lin

doi:10.1016/j.image.2004.08.001

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…2, the ASIP was synthesized for the clock frequencies of 3 MHz for the MV-FAST algorithm, 5 MHz for the 4SS algorithm and 50 MHz for the bottom line case of the FSBM algorithm. Nevertheless, the maximum operating frequency of the proposed motion estimators is significantly lower than the frequency of the ±1 full-search based processor presented in [2].…”

Section: Resultsmentioning

confidence: 85%

“…At the same time, new dataadaptive fast algorithms have also been proposed for software ME. On the hardware side, some of the proposed architectures exploit the input data variations to dynamically configure the search-window size of the FSBM [7] algorithm, while other approaches allow to guide the search pattern according to the gradient-descent direction, based on a ±1 full-search that implements a fixed 3 × 3 square search window [2]. For software implementations, efficient search algorithms have been developed, by taking advantage of temporal and spacial correlations of the motion vectors in order to adapt and optimize the search pattern, thus avoiding unnecessary computations and memory accesses.…”

Section: Introductionmentioning

confidence: 99%

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Application Specific Instruction Set Processor for Adaptive Video Motion Estimation

Momcilovic

Dias

Roma

et al. 2006

9th EUROMICRO Conference on Digital System Design (DSD'06)

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Motion estimation is the most demanding operation of a video encoder, corresponding to at least 80% of the overall computational cost. With the proliferation of portable handheld devices that support digital video coding, dataadaptive motion estimation algorithms have been required to dynamically configure the search pattern not only to avoid unnecessary computations and memory accesses but also to save energy. This paper proposes an Application Specific Instruction Set Processor (ASIP) to implement data-adaptive motion estimation algorithms, that is characterized by a specialized data-path and minimum and optimized instruction set. Due to its low-power nature, this architecture is specially adequate to develop motion estimators for portable, mobile and battery supplied devices. A cycle-based accurate simulator was also developed for the proposed ASIP and fast and data-adaptive search algorithms have been implemented, namely, the four-step search and the motion vector field adaptive search algorithms. Based on the proposed ASIP and the considered adaptive algorithms, several motion estimators were synthesized in 0 .13 μm CMOS technology. Experimental results show that very-low power adaptive motion estimators have been achieved to encode QCIF video sequences.

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Section: Resultsmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Application Specific Instruction Set Processor for Adaptive Video Motion Estimation

Momcilovic

Dias

Roma

et al. 2006

9th EUROMICRO Conference on Digital System Design (DSD'06)

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“…In this analysis, the Sobel operator obtained good results. In this paper, the signed results of the Sobel operator in both directions are only transformed into the feature list representation form (3) if the absolute feature value exceeds a constant predefined threshold value. Finally, both feature lists have to be concatenated.…”

Section: Feature Listmentioning

confidence: 99%

“…For the medical application, the human retinal blood vessel image series from five test persons (see Figure 2) [23,39] are used. 3 The image series includes 21 to 26 single grayscale fundus images of five healthy subjects. The images have a size of 768 × 576 pixels.…”

Section: Test Imagesmentioning

confidence: 99%

“…Furthermore, extensive changes in brightness, contrast, or strong outliers cause false results [1]. Because of this, optimized algorithms have been developed, namely, the cross-correlation algorithm based on least squared error [2], the block-matching algorithms [3], techniques based on the Fourier transform [4], waveletbased techniques [5], and feature-based techniques [2,[6][7][8][9][10]. These algorithms are used for many problems such as motion estimation [11], video coding [12], target detection [13], character recognition [8], image registration [14], or image fusion [15].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms

Maschotta

Boymann

Hoppe³

2007

EURASIP J. Adv. Signal Process.

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This paper presents a feature-list cross-correlation algorithm based on: a common feature extraction algorithm, a transformation of the results into a feature-list representation form, and a list-based cross-correlation algorithm. The feature-list cross-correlation algorithms are compared with known results of the common cross-correlation algorithms. Therefore, simple test images containing different objects under changing image conditions and with several image distortions are used. In addition, a medical application is used to verify the results. The results are analyzed by means of curve progression of coefficients and curve progression of peak signal-to-noise ratio (PSNR). As a result, the presented feature list cross-correlation algorithms are sensitive to all changes of image conditions. Therefore, it is possible to separate objects that are similar but not equal. Because of the high quantity of feature points and the strong PSNR, the loss of a few feature points does not have a significant influence on the detection results. These results are confirmed by a successfully applied medical application. The calculation time of the feature list cross-correlation algorithms only depends on the length of the feature-lists. The amount of feature points is much less than the number of pixels in the image. Therefore, the feature-list cross-correlation algorithms are faster than common cross-correlation algorithms. Better image conditions tend to reduce the size of the feature-list. Hence, the processing time decreases considerably.

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A Parallel Algorithm for Advanced Video Motion Estimation on Multicore Architectures

Momcilovic

Sousa

2008

2008 International Conference on Complex, Intelligent and Software Intensive Systems

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A simple and efficient block motion estimation algorithm based on full-search array architecture

Cited by 8 publications

References 12 publications

Application Specific Instruction Set Processor for Adaptive Video Motion Estimation

Application Specific Instruction Set Processor for Adaptive Video Motion Estimation

Comparison of Feature-List Cross-Correlation Algorithms with Common Cross-Correlation Algorithms

A Parallel Algorithm for Advanced Video Motion Estimation on Multicore Architectures

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