Tag: Video

 
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A Prototyping Virtual Socket System-On-Platform Architecture with a Novel ACQPPS Motion Estimator for H.264 Video Encoding Applications

Abstract

H.264 delivers the streaming video in high quality for various applications. The coding tools involved in H.264, however, make its video codec implementation very complicated, raising the need for algorithm optimization, and hardware acceleration. In this paper, a novel adaptive crossed quarter polar pattern search (ACQPPS) algorithm is proposed to realize an enhanced inter prediction for H.264. Moreover, an efficient prototyping system-on-platform architecture is also presented, which can be utilized for a realization of H.264 baseline profile encoder with the support of integrated ACQPPS motion estimator and related video IP accelerators. The implementation results show that ACQPPS motion estimator can achieve very high estimated image quality comparable to that from the full search method, in terms of peak signal-to-noise ratio (PSNR), while keeping the complexity at an extremely low level. With the integrated IP accelerators and optimized techniques, the proposed system-on-platform architecture sufficiently supports the H.264 real-time encoding with the low cost.

Download A Prototyping Virtual Socket System-On-Platform Architecture with a Novel ACQPPS Motion Estimator for H.264 Video Encoding Applications

 

Yifeng Qiu and Wael Badawy, “A Prototyping Virtual Socket System-On-Platform Architecture with a Novel ACQPPS Motion Estimator for H.264 Video Encoding Applications” EURASIP Journal on Embedded Systems, Volume 2009

Link to the list of other Peer Journal Publications

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Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Abstract:

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:23 ,  Issue: 2 )

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A Low Power Architecture for HASM Motion Tracking

This paper proposes low power VLSI architecture for motion tracking that can be used in online video applications such as in MPEG and VRML. The proposed architecture uses a hierarchical adaptive structured mesh (HASM) concept that generates a content-based video representation. The developed architecture shows the significant reducing of power consumption that is inherited in the HASM concept. The proposed architecture consists of two units: a motion estimation and motion compensation units.

The motion estimation (ME) architecture generates a progressive mesh code that represents a mesh topology and its motion vectors. ME reduces the power consumption since it (1) implements a successive splitting strategy to generate the mesh topology. The successive split allows the pipelined implementation of the processing elements. (2) It approximates the mesh nodes motion vector by using the three step search algorithm. (3) and it uses parallel units that reduce the power consumption at a fixed throughput.

The motion compensation (MC) architecture processes a reference frame, mesh nodes and motion vectors to predict a video frame using affine transformation to warp the texture with different mesh patches. The MC reduces the power consumption since it uses (1) a multiplication-free algorithm for affine transformation. (2) It uses parallel threads in which each thread implements a pipelined chain of scalable affine units to compute the affine transformation of each patch.

The architecture has been prototyped using top-down low-power design methodology. The performance of the architecture has been analyzed in terms of video construction quality, power and delay.

Wael Badawy and Magdy Bayoumi “A Low Power Architecture for HASM Motion Tracking,” The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, May 2004, Vol. 37, Issue 1, pp. 111-127

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A Prototyping Virtual Socket System-On-Platform Architecture with a Novel ACQPPS Motion Estimator for H.264 Video Encoding Applications

Abstract

H.264 delivers the streaming video in high quality for various applications. The coding tools involved in H.264, however, make its video codec implementation very complicated, raising the need for algorithm optimization, and hardware acceleration. In this paper, a novel adaptive crossed quarter polar pattern search (ACQPPS) algorithm is proposed to realize an enhanced inter prediction for H.264. Moreover, an efficient prototyping system-on-platform architecture is also presented, which can be utilized for a realization of H.264 baseline profile encoder with the support of integrated ACQPPS motion estimator and related video IP accelerators. The implementation results show that ACQPPS motion estimator can achieve very high estimated image quality comparable to that from the full search method, in terms of peak signal-to-noise ratio (PSNR), while keeping the complexity at an extremely low level. With the integrated IP accelerators and optimized techniques, the proposed system-on-platform architecture sufficiently supports the H.264 real-time encoding with the low cost.

Download A Prototyping Virtual Socket System-On-Platform Architecture with a Novel ACQPPS Motion Estimator for H.264 Video Encoding Applications

 

Yifeng Qiu and Wael Badawy, “A Prototyping Virtual Socket System-On-Platform Architecture with a Novel ACQPPS Motion Estimator for H.264 Video Encoding Applications” EURASIP Journal on Embedded Systems, Volume 2009

Link to the list of other Peer Journal Publications

+

Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Abstract:

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:23 ,  Issue: 2 )