&lt;title&gt;ISO-IEC MPEG-2 software video codec&lt;/title&gt;

Eckart, Stefan; Fogg, Chad E.

doi:10.1117/12.206349

Cited by 74 publications

(37 citation statements)

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“…Some algorithms attempt to reduce the number of searching positions in block matching, e.g., three-step search [9], new three-step search [10], four-step search [11], diamond search [12], [13], cross-diamond search [14], kite cross-diamond search [15], and others. Some others seek to reduce the number of pixels used in the matching distortion calculations, e.g., partial distortion search (PDS) [16], alternative sub-sampling search algorithm [17], normalized PDS [18], adjustable PDS [19], dynamic search window adjustment [20], and others. There are also some hybrid methods combining multiple techniques, e.g., motion vector field adaptive search technique (MVFAST) [21], predictive MVFAST [22], UMHS [4], [5], and EPZS [1], [6].…”

Section: ) Traditional Motion Estimation Algorithmsmentioning

confidence: 99%

Sensor-Assisted Video Encoding for Mobile Devices in Real-World Environments

Chen

Zhao

Rahmati

et al. 2011

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In this paper, we present a comprehensive study on sensor-assisted video encoding (SaVE) schemes for video capturing on mobile devices in real-world environments. Our purpose is to reduce the computational complexity of video encoding by leveraging sensors that are increasingly available on mobile devices, e.g., accelerometers and digital compasses. Motion estimation is a key component of video encoding. In this paper, SaVE calculates the rotational movement of a camera (on mobile devices) and then infers the global motion in the camera imager. SaVE subsequently employs the estimated global motion as predictors to simplify motion estimation algorithms for state-of-the-art H.264/AVC video coding. We have constructed a prototype of SaVE and evaluated its performance with a pair of accelerometers, a digital compass, and their combination. Our experimental results show that SaVE can significantly reduce the computations of motion estimation while achieving equal or better video quality. Our results also show that SaVE has a strong noise-resistant capability. Therefore, it can be practically employed in real-world environments.

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Section: ) Traditional Motion Estimation Algorithmsmentioning

confidence: 99%

Sensor-Assisted Video Encoding for Mobile Devices in Real-World Environments

Chen

Zhao

Rahmati

et al. 2011

IEEE Trans. Circuits Syst. Video Technol.

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“…1 In order to solve this problem, many fast algorithms have been developed, and partial difference elimination ͑PDE͒ algorithms [1][2][3][4] are good examples. PDE algorithms reduce computations by eliminating impossible candidates before the complete matching error calculation between matching and candidate blocks.…”

Section: Introductionmentioning

confidence: 99%

“…PDE algorithms reduce computations by eliminating impossible candidates before the complete matching error calculation between matching and candidate blocks. PDE algorithms have also been improved by selecting a low initial block matching error 1,2 or arranging the line/sub-block matching order in a candidate block. 3,4 In this letter, we propose a fast PDE algorithm for reducing additional computational costs based on the property of block matching error.…”

Section: Introductionmentioning

confidence: 99%

Fast partial difference elimination algorithm based on block matching error prediction

Shin

Lee

2007

Opt. Eng

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Abstract. We introduce a fast partial difference elimination ͑PDE͒ algorithm for motion estimation ͑ME͒. The basic idea of the proposed approach is to eliminate invalid candidates earlier by predicting a total matching error between matching and candidate blocks. The matching error prediction is performed by using a partially computed matching error between the blocks. Experimental results show that the proposed algorithm can significantly save about 40% of the averaged computational costs versus the conventional PDE algorithm for ME at the cost of ignorable image quality degradation, whose average value is 0.0012 dB.

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“…However, the computational complexity of FSA is always too high for real-time implementation. Therefore, many fast motion estimation algorithms to reduce the computation of the FSA have been proposed over the past few decades [1][2][3][4][5][6][7][8]. These fast motion estimation using BMAs can be classified into two groups.…”

Section: Introductionmentioning

confidence: 99%

“…One is the lossy motion estimation algorithm, and the other is the lossless one. The lossy motion estimation (e.g., NTSS [1], DS [2] and HEXBS [3]) has some degradation of predicted images compared with the conventional FSA, and the lossless motion estimation obtains no degradation of predicted images compared with the conventional FSA (e.g., SEA [4], PDE [5] and ASSA [6]). Recently, the improved versions [7][8][9] for lossy motion estimation using partial distortion elimination algorithm (PDE) have been proposed for more accurate prediction.…”

Section: Introductionmentioning

confidence: 99%

Efficient Motion Estimation using Sorting-Based Partial Distortion Search

Wang

2006

2006 IEEE International Conference on Multimedia and Expo

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An efficient motion-estimation algorithm based on partial block distortion using sorted significant features including bit-plane and mean is proposed. The proposed algorithm can obtain relatively accurate motion vectors with a reduced computational load. Simulation results show that the proposed method achieves its MSE performance very close to the full search method, while requiring only 6-8% of the computation needed by the full search. Furthermore, the performance of our method is better than other algorithms based on partial block distortion search.

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<title>ISO-IEC MPEG-2 software video codec</title>

Cited by 74 publications

References 0 publications

Sensor-Assisted Video Encoding for Mobile Devices in Real-World Environments

Sensor-Assisted Video Encoding for Mobile Devices in Real-World Environments

Fast partial difference elimination algorithm based on block matching error prediction

Efficient Motion Estimation using Sorting-Based Partial Distortion Search

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