A low energy adaptive hardware for H.264 multiple reference frame motion estimation

Aysu, Aydın; Sayilar, Gokhan; Hamzaoğlu, İlker

doi:10.1109/tce.2011.6018897

Cited by 13 publications

(7 citation statements)

References 10 publications

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“…The experiments are performed on a computer using Matlab, and not on a DSP/DIP processor using C or other hardware languages. In real applications with optimization, the computational costs can be in micro or nano seconds and the information of implementing the video algorithms on a hardware kit with timing details can be found in [36], [37]. Therefore, from the mobile devices point of view, the overhead caused by the computational costs is imperceptible as the proposed scheme just involves implementing a motion estimation algorithm and an adaptive filter when it receives an alert for a missing video frame.…”

Section: Computational Complexitymentioning

confidence: 99%

Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos

Usman

Lam

et al. 2019

IEEE Trans. Cloud Comput.

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The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bitrates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.

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Section: Computational Complexitymentioning

confidence: 99%

Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos

Usman

Lam

et al. 2019

IEEE Trans. Cloud Comput.

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“…square and diamond search is used to determine the BRF of the current block, and this BRF is searched using FS algorithm in literature.In the literature [5],Arithmatic encoding and Run length encoding algorithm is implemented.here,Discrete cosine transform is used to convert each pixel value into frequency domain.RLE algorithm is easy to implement but not gives high compression ratio.In the literature [1],Multiple reference frame motion estimation is used.This algorithm uses number of reference frames for each macroblock and early termination.This hardware has less energy consumption.…”

Section: Previous Workmentioning

confidence: 99%

“…Sum of Absolute Differences (SAD) is the most preferred block matching criterion because of its low computational cost.The block with minimum SAD is then taken as Best reference block.Among the BM algorithm,Full search algorithm achieves the best performance since it searches all search locations in the given search range. But, its computational complexity is very high,it requires huge hardware and large candidate blocks [1].Many Frequency based techniques are also used which uses Fourier Transform.But if we compare fourier Transform with DWT.Then,DWT is effective because it has multiresolution capability. Wavelets are signals which are local in time and scale and generally have an irregular shape.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of algorithm for video compression

Gaoture¹,

Nagrare²

2014

International Conference on Information Communication and Embedded Systems (ICICES2014)

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Nowadays, Video Compression has gaining important because of its application in different field like internet. So, there is different algorithm used to achieve the compression of Video. Video Compression algorithms combine Spatial image compression and Temporal motion compression. The sequence of frames contains Spatial and Temporal redundancies that Video Compression algorithms attempt to eliminate or code in smaller size. In this paper, We present a design of Binary motion vector technique with Pruning Discrete Wavelet Transform for the compression of AVI video.Here, Binary motion vector technique used for searching the Best matching block.This technique requires less candidates block than other motion vector technique.Pruning based DWT uses thresholding which will enhance the compression ratio with desirable Peak Signal to Noise ratio .This algorithm will be going to simulate on XilinxISE13.1 and implement on SPARTEN3 FPGA.

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“…Here, we list some of the optimizations proposed for the H.264 codec to implement on an FPFA platform. In [14], low complexity and low energy adaptive hardware for H.264 with multiple frame reference motion estimation is proposed and implemented on an FPGA platform. A bioinspired custom-made reconfigurable hardware architecture is proposed in [15,16] for efficient computation of motion between image sequences.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based Portable Ultrasound Scanning System with Automatic Kidney Detection

et al. 2015

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Bedsides diagnosis using portable ultrasound scanning (PUS) offering comfortable diagnosis with various clinical advantages, in general, ultrasound scanners suffer from a poor signal-to-noise ratio, and physicians who operate the device at point-of-care may not be adequately trained to perform high level diagnosis. Such scenarios can be eradicated by incorporating ambient intelligence in PUS. In this paper, we propose an architecture for a PUS system, whose abilities include automated kidney detection in real time. Automated kidney detection is performed by training the Viola–Jones algorithm with a good set of kidney data consisting of diversified shapes and sizes. It is observed that the kidney detection algorithm delivers very good performance in terms of detection accuracy. The proposed PUS with kidney detection algorithm is implemented on a single Xilinx Kintex-7 FPGA, integrated with a Raspberry Pi ARM processor running at 900 MHz.

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A low energy adaptive hardware for H.264 multiple reference frame motion estimation

Cited by 13 publications

References 10 publications

Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos

Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos

Design and implementation of algorithm for video compression

FPGA-Based Portable Ultrasound Scanning System with Automatic Kidney Detection

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