Fast motion estimation using modified orthogonal search algorithm for video compression

Metkar, Shilpa P.; Talbar, Sanjay N.

doi:10.1007/s11760-009-0104-9

Cited by 17 publications

(5 citation statements)

References 18 publications

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“…Modified Orthogonal search (MOS) algorithm was introduced by Metkar et al [17] in 2010, meant to solve the small motion estimation problem. It adds 8 new search points as a 3 × 3 square around the initial point in the first search step ( Figure 3(b)).…”

Section: Enhanced Modified Orthogonal Searchmentioning

confidence: 99%

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

Yan¹,

Cho²,

Welsen³

2017

JCC

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This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% -30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.

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Section: Enhanced Modified Orthogonal Searchmentioning

confidence: 99%

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

Yan¹,

Cho²,

Welsen³

2017

JCC

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“…For all the standard video compression techniques, video coding can be obtained by taking advantage of the redundant information in any video [Kim, 2010;ITU-T and ISO/IEC, 2003;Al-Mualla et al, 2002;Metkar and Talbar, 2010;ISO/IEC, 1993;Sayood, 2006;Chanyul, 2010].…”

Section: Redundant Informationmentioning

confidence: 99%

“…There are several inter-frame compression methods of varying degrees of complexity, such as subsampling coding, difference coding, block-based difference coding and motion compensation [ISO/IEC, 1993;Metkar and Talbar, 2010]. This thesis deals with temporal redundancy and attempts to enhance the complexity computations that come from inter-frame compression, as shown in Chapter 3.…”

Section: Temporal Redundancymentioning

confidence: 99%

“…At the decoder, the received MVs and RPE will be decoded and utilised to form MCP from the reconstructed reference frame and use the reconstructed RPE to reconstruct the current frame. The matching measure is usually determined using a Block Distortion Measure (BDM) the least BDM is considered to be the one matching the current macroblock [Metkar and Talbar, 2010].…”

Section: Block Matching Motion Estimationmentioning

confidence: 99%

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Enhanced Computation Time for Fast Block Matching Algorithm

Ahmed

Hussain

Al‐Jumeily

2011

2011 Developments in E-Systems Engineering

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i ACKNOWLEDGMENTSMy thanks go firstly, as it should always be, to Allah who gave me the energy, health, and courage to spend a lot of determination and time until I completed this work.My most sincere gratitude and appreciation to my supervisor Dr Abir Hussain for all her patience, valuable advice, discussions, convincing arguments and more during the life of this thesis, I wish her all the best for the future. I also owe a great debt to Dr. Dhiya Al-Jumeily for his support, advice and encouragement from the beginning until now and I would like to thank him for his constructive comments on my thesis. Motion compensated predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50-80% of the video encoding complexity. This technique has been adopted by all of the existing international video coding standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks; each target macroblock of the current frame is compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm were developed to reduce the computation complexity.This thesis focuses on two classifications: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the full search is decreased while the resolution of the predicted frames is the same as for the full search. The second is called the lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the full search.

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“…Although this algorithm is the best in terms of quality of the predicted frame and simplicity, it is computationally intensive. In the past two decades, several fast search methods for motion estimation have been introduced to reduce the computational complexity of block matching, for examples, two dimensional Logarithmic Search (LOGS) (Nam et al, 2000b), three-Step Search (3SS) (Koga et al, 1981), four Step Search (4SS) (Lai-Man and Wing-Chung, 1996), Modified Orthogonal Search Algorithm (MOSA) (Metkar and Talbar, 2009). Among these algorithms, 3SS becomes most popular one for low bit rate application owing to its simplicity and effectiveness…”

Section: Motion Estimationmentioning

confidence: 99%

Motion Detection and Projection Based Block Motion Estimation Using the Radon Transform for Video Coding

Metkar¹

2010

Journal of Computer Science

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Problem statement: Motion estimation and compensation is the most computationally complex module of video coder. In this study, an innovative algorithm was proposed for a further complexity reduction of the Motion Estimation (ME) module of video coder by employing motion detection prior to motion compensation. Approach: A Motion Detection (MD) module can be added to the video coder in order to decide whether the current block contains motion or is with zero motion. This study propounded a MD module that depends on motion activity. Generally, the correlation of the two consecutive frames is a good criterion to measure motion activity. We applied the correlation as a threshold to detect the motion activity. To assure the correct motion vector and thus better video quality, to calculate motion vector of motion blocks this study also proposed a new block matching motion estimation criterion based on the Radon transform using projection-matching method. However, computationally complex, the method had the ability to be implemented in real time by using pipeline architecture. Results: A comparative result showed that the MD module reduced the number of search points for motion estimation and compared with some well-known algorithm that uses minimum absolute difference criterion, the new criterion can provide much higher performance. Conclusion: The result showed that this proposed scheme can simplify the encoder complexity maintaining good video quality

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Fast motion estimation using modified orthogonal search algorithm for video compression

Cited by 17 publications

References 18 publications

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

Enhanced Computation Time for Fast Block Matching Algorithm

Motion Detection and Projection Based Block Motion Estimation Using the Radon Transform for Video Coding

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