MapReduce-Based Distributed Video Encoding Using Content-Aware Video Segmentation and Scheduling

Jeon, Myunghoon; Kim, Namgi; Lee, Byoung-Dai

doi:10.1109/access.2016.2616540

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…Taking into account the previous point and after an exhaustive search we can indicate that there are few published papers related to RAW video encoding over cloud environments, and they are mainly based on the MapReduce paradigm [14,15]. In [14], the authors propose the Split&Merge architecture for deploying video encoding over cloud infrastructures.…”

Section: Related Workmentioning

confidence: 99%

A Cloud-Based Distributed Architecture to Accelerate Video Encoders

et al. 2020

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Nowadays, video coding and transcoding have a great interest and important impact in areas such as high-definition video and entertainment, healthcare and elderly care, high-resolution video surveillance, self-driving cars, or e-learning. This growing demand for high-resolution video boosts the proposal of new codecs and the development of their encoders that require high computational requirements. Therefore, new strategies are needed to accelerate them. Cloud infrastructures offer interesting features for video coding, such as on-demand resource allocation, multitenancy, elasticity, and resiliency. This paper proposes a cloud-based distributed architecture, where the network and the storage layers have been tuned, to accelerate video encoders over an elastic number of worker encoder nodes. Moreover, an application is developed and executed in the proposed architecture to allow the creation of encoding jobs, their dynamic assignment, their execution in the worker encoder nodes, and the reprogramming of the failed ones. To validate the proposed architecture, the parallel execution of existing video encoders, x265 for H.265/HEVC and libvpx-vp9 for VP9, has been evaluated in terms of scalability, workload, and job distribution, varying the number of encoder nodes. The quality of the encoded videos has been analyzed for different bit rates and number of frames per job using the Peak Signal-to-Noise Ratio (PSNR). Results show that our proposal maintains video quality compared with the sequential encoding while improving encoding time, which can decrease near 90%, depending on the codec and the number of encoder nodes.

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Section: Related Workmentioning

confidence: 99%

A Cloud-Based Distributed Architecture to Accelerate Video Encoders

et al. 2020

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“…However, it degrades the bitrate largely, which is quite difficult to achieve back completely. In [13], Segmentation and Scheduling video encoding based on distributed Map Reduce architecture presented to speed up the encoding process by using advanced distributed technique. In this paper, video segmentation technique used to provide better efficiency and to change the encoding segment order an efficient scheduling scheme adopted.…”

Section: Video Encoding Issuesmentioning

confidence: 99%

“…However, the computational complexity is very high for this standard, which causes its degradation in performance [2]. Another biggest disappointment with this technique is its slowness (takes more computational time) [13]. Map Reduce based distributed algorithm [13] used to speed-up the encoding process.…”

Section: Introductionmentioning

confidence: 99%

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Efficient High Quality Video Assessment Using Salient Features

Rekha¹,

Kumar²

2017

IJIVP

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High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.

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“…In [7], a distributed Map Reduce technique presented to make faster the encoding process based on scheduling and segmentation video encoding. In [8], to identify key-points and achieve efficient feature extraction a Key point Encoding technique presented at lower bitrates.…”

Section: Introductionmentioning

confidence: 99%

High Quality Video Assessment Using Salient Features

Rekha¹,

Av²

2017

IJEECS

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<p class="Abstract">An efficient modified video compression HEVC technique based on high quality assessment saliency features presented for the assessment of high quality videos. To create an efficient saliency map we extract global temporal alignment component and robust spatial components. To obtain high quality saliency here, we combine spatial saliency features and temporal saliency features together for different macroblocks in association with transformed residuals. In this way, our saliency model outperforms all the existing techniques. In this paper, we have generated high reconstruction quality video after compression considering SFU dataset. Our experimental results outperforms all the existing techniques in terms of saliency map detection, PSNR and high-resolution quality.</p>

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MapReduce-Based Distributed Video Encoding Using Content-Aware Video Segmentation and Scheduling

Cited by 10 publications

References 24 publications

A Cloud-Based Distributed Architecture to Accelerate Video Encoders

A Cloud-Based Distributed Architecture to Accelerate Video Encoders

Efficient High Quality Video Assessment Using Salient Features

High Quality Video Assessment Using Salient Features

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