• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.809-810
• /
• 2006

An H.264 algorithm is commonly used for video compression applications. This algorithm requires a large number of data computations, for example, the sum of absolute difference (SAD) operation. We analyzed H.264 reference encoding workloads. The H.264 encoding program has 8.78% SAD operation. The SAD operation is to sum up 16 difference-values in H.264 $4{\times}4$ sub-blocks. In order to accelerate SAD operations, we implemented an application specific instruction-set processor (ASIP) that can execute SAD and data transfer instructions. The proposed coprocessor has an absolute value generator and a carry save adder (CSA) unit to sum up 8 difference-values per one clock cycle. We completed SAD operation in 2 clock cycles. Experimental results show that the performance is improved by 34% of total execution time.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.5 no.4
• /
• pp.179-187
• /
• 2009

In this paper, an efficient multi-level successive elimination algorithm using the locality in block was proposed for motion estimation. If SAD(sum of absolute difference) is calculated from large absolute difference values to small absolute difference values, SAD is increased rapidly. So, partial distortion elimination in SAD calculation can be done very early. Hence, the computations of SAD calculation can be reduced. In this paper, an efficient algorithm to calculate SAD from large absolute difference values to small absolute difference values by using the locality in block. Experimental results show that the proposed algorithm is an efficient algorithm with 100% motion estimation accuracy for the motion estimation of motion vectors.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.23-25
• /
• 2007

The partial distortion elimination (PDE) scheme is used to decrease the sum of absolute difference (SAD) computational complexity, since the SAD calculation has been taken much potion of the video compression. In motion estimation (ME) based on PDE, it is ideal that the initial value of SAD in summing performance has large value. The traditional scan order methods have many operation time and high operational complexity because these adopted the division or multiplication. In this paper, we introduce the new scan order and search order by using only adder. We define the average value which is called to rough average value (RAVR). Which is to reduce the computational complexity and increase the operational speed and then we can obtain the improvement of SAD performance. And also this RAVR is used to decide the search order sequence, since the difference RAVR between the current block and candidate block is small then this candidate block has high probability to suitable candidate. Thus, our proposed algorithm combines above two main concepts and suffers the improving SAD performance and the easy hardware implementation methods.

• Journal of the Semiconductor & Display Technology
• /
• v.8 no.4
• /
• pp.95-100
• /
• 2009

This paper presents an efficient disparity matching, using sum of absolute difference (SAD) and dynamic programming (DP) algorithm. This algorithm makes use of one of area-based algorithm which is the absolute sum of the pixel difference corresponding to the window size. We use the information of the right eye brightness (B) and the left eye brightness to get an best matching results and apply the results to the left eye image using the window go by the brightness of the right eye image. This is that we can control the brightness. The major feature of this algorithm called SAD+DP+B is that although Root Mean Square (RMS) performance is slightly less than SAD+DP, due to comparing original image, its visual performance is increased drastically for matching the disparity map on account of its matching compared to SAD+DP. The simulation results demonstrate that the visual performance can be increased and the RMS is competitive with or slightly higher than SAD+DP.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.171-176
• /
• 2019

Drones are highly utilized because they can efficiently acquire long-distance videos. In drone operation, the speed, which is the magnitude of the velocity, can be set, but the moving direction cannot be set, so accurate information about the drone's movement should be estimated. In this paper, we estimate the velocity of the drone moving at a constant speed and direction. In order to estimate the drone's velocity, the displacement of the target frame to minimize the sum of absolute difference (SAD) of the reference frame and the target frame is obtained. The ground truth of the drone's velocity is calculated using the position of a certain matching point over all frames. In the experiments, a video was obtained from the drone moving at a constant speed at a height of 150 meters. The root mean squared error (RMSE) of the estimated velocities in x and y directions and the RMSE of the speed were obtained showing the reliability of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.101-104
• /
• 2015

Motion vector is that comparing a frame between previous frame and current one about how much moved. Using this motion vector, if move the image object of current frame to former frame, it could be corrected to shake from hand and camera shaking. On this thesis, compared efficiency of block matching using SAD(Sum of Absolute Difference) equation as picking out the motion vector, matching using phase correlation, matching using feature point, block matching using bitplane.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.1
• /
• pp.107-115
• /
• 2014

The reduced-bit transform based bit-plane matching algorithm (BPM) can obtain the block matching result through its simple calculation and hardware design compared to the conventional block matching algorithms (BMAs), but the block matching accuracy of BPMs is somewhat low. In this paper, reduced-bit transform based sum of the absolute difference (R-SAD) is proposed to improve the block matching accuracy in comparison with the conventional BPMs and it is shown that the matching process can be obtained using the logical operations. Firstly, this method transforms the current and the reference images into their respective 2-bit images and then a truth table is obtained from the relation between input and output 2-bit images. Next, a truth table is simplified by Karnaugh map and the absolute difference is calculated by using simple logical operations. Finally, the simulation results show that the proposed R-SAD can obtain higher accuracy in block matching results compared to the conventional BPMs through the PSNR analysis in the motion compensation experiments.

• Journal of Satellite, Information and Communications
• /
• v.7 no.2
• /
• pp.8-12
• /
• 2012

Area-based stereo matchng algorithms are widely used for image analysis for stereo vision. SAD (Sum of Absolute Difference) algorithm is one of well known area-based stereo matchng algorithms with the characteristics of data intensive computing application. Therefore, it requires very high computation capabilities and its processing speed becomes very slow with software realization. This paper proposes a fast SAD algorithm utilizing SSE (Streaming SIMD Extensions) instructions based on SIMD (Single Instruction Multiple Data) parallism. CPU supporing SSE instructions has 16 XMM registers with 128 bits. For the performance evaluation of the proposed scheme, we compare the processing speed between SAD with/without SSE instructions. The proposed scheme achieves four times performance improvement over the general SAD, which shows the possibility of the software realization of real time SAD algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.47 no.7
• /
• pp.70-77
• /
• 2010

Motion estimation occupies most of the required computation in video compression, and many fast search algorithms were propsoed to reduce huge computation. SAD (sum-of-absolute difference) calculation is the most computation-intensive process in the motion estimation. Early termination is widely used in SAD calculation, where SAD calculation is terminated and it proceeds to next search position if partial SAD during SAD calculation exceeds current minimum SAD. In this paper, we proposed a modified 3-step search algorithm for effective early termination where only search order of search positions are adaptive rearranged. Simulation results show that the proposed motion estimation algorithm reduces computation by 17~30% over conventional 3-step search algorithm without extra computation, while maintaining same performance.

• Journal of KIISE:Software and Applications
• /
• v.29 no.1_2
• /
• pp.98-113
• /
• 2002

In this paper, we present advanced algorithms to reduce the computations of block matching algorithms for motion estimation in video coding. Advanced multi-level successive elimination algorithms(AMSEA) are based on the Multi-level successive elimination algorithm(MSEA)[1]. The first algorithm is that when we calculate the sum of absolute difference (SAD) between the sum norms of sub-blocks in MSEA, we use the partial distortion elimination technique. By using the first algorithm, we can reduce the computations of MSEA further. In the second algorithm, we calculate SAD 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 SAD calculation can occur early. So, the computations of MSEA can be reduced. In the third algorithm, we can estimate the elimination level of MSEA. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is a very fast block matching algorithm with nearly 100% motion estimation accuracy. Experimental results show that AMSEA are very efficient algorithms for the estimation of motion vectors.