H.264 Video Decoder Optimization on ARM Cortex-A8 with NEON

Pujara, Chirag; Modi, Anurag; Sandeep, G. S.; Inamdar, Shilpa; Kolavil, Deepa; Tholath, Vidhu

doi:10.1109/indcon.2009.5409460

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…With the target platform being an ARM based CPU with ARM's NEON [20] capabilities which allows for single instruction multiple data (SIMD) operations, the approach chosen was to enhance gray scale conversions using NEON intrinsic instructions.…”

Section: Real-time Implementationmentioning

confidence: 99%

Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

Salehian

Far

2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Fatigue induced vehicle accidents have seen an increase in the last few decades. Fatigue monitoring using noninvasive and real time image processing and computer vision techniques have shown great promise and are an active research area. To that extent, in the proposed work a blink detection algorithm is proposed that serves as a visual cue that may be correlated to the state of fatigue of the driver. Using a complimentary but independent approach, shape analysis and histogram analysis are carried out in parallel to perform the blink detection task. Close to real time performance and a high level of accuracy in controlled settings show great promise of such approach in enhancing the monitoring of the driver's blinking patterns. One of the main constraints of using such algorithm in a real world setting is the minimized processing time required to allow for sufficient driver response time. In this work implementation of the algorithm is described using optimization techniques to meet such latency requirements. The validation of the algorithm was carried out by visual inspection of the video sequences in terms of precision and accuracy. The presented blink detection algorithm has a precision rate of 84% and an accuracy rate of 69% obtained through using 12 sequences of different duration videos in varying lighting conditions using a small sample of participants.

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Section: Real-time Implementationmentioning

confidence: 99%

Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

Salehian

Far

2015

International Conferences on Software Engineering and Knowledge Engineering

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“…There are several works on acceleration of H.264 [1] and VC-1 [2] decoder by utilizing SIMD capability of ARM NEON platform. In our previous work, we use the similar strategy to accelerate AVS decoder [3], but HD video can't still be decoded in real-time only by this kind of acceleration.…”

Section: Introductionmentioning

confidence: 99%

High definition IEEE AVS decoder on ARM NEON platform

Wang

Wan

Wang

et al. 2013

2013 IEEE International Conference on Image Processing

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Nowadays, mobile devices are capable of displaying video up to HD resolution. In this paper, we propose two acceleration strategies for Audio Video coding Standard (AVS) software decoder on multi-core ARM NEON platform. Firstly, data level parallelism is utilized to effectively use the SIMD capability of NEON and key modules are redesigned to make them SIMD friendly. Secondly, a macroblock level wavefront parallelism is designed based on the decoding dependencies among macroblocks to utilize the processing capability of multiple cores. Experiment results show that AVS (IEEE 1857) HD video stream can be decoded in real-time by applying the proposed two acceleration strategies.

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“…There are several related works on acceleration of H.264 [3] and VC-1 [4] decoder by utilizing SIMD capability of ARM NEON platform. In our previous work, we use the similar strategy to accelerate AVS decoder [5].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of HEVC transform and inverse transform on ARM NEON platform

Hui

Wang

et al. 2013

2013 International Symposium on Intelligent Signal Processing and Communication Systems

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HEVC (High Efficiency Video Coding) has recently been published as the next generation video coding standard. Compared with previous standards, the coding efficiency is greatly improved at the cost of much higher codec complexity. On the other hand, ARM with SIMD (Single Instruction Multiple Data) instructions is widely deployed on mobile platform, which makes it feasible to accelerate multimedia applications by software. However, the accelerations are not straight forward, and the modules have to be carefully redesigned to utilize SIMD instructions efficiently. Transform and inverse transform modules are frequently called in HEVC codec. In this paper, we propose an acceleration method of the transform and inverse transform by utilizing the data level parallelism on NEON platform. Experiment results show that the speed of these modules has been increased considerably.

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H.264 Video Decoder Optimization on ARM Cortex-A8 with NEON

Cited by 6 publications

References 3 publications

Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

Embedded Real Time Blink Detection System for Driver Fatigue Monitoring

High definition IEEE AVS decoder on ARM NEON platform

Acceleration of HEVC transform and inverse transform on ARM NEON platform

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