Frame memory compression for high-resolution video coding

2019 ◽  
pp. 227-255
Author(s):  
Yibo Fan ◽  
Chenhao Gu ◽  
Tianwen Yang ◽  
Ruixue Lei
Keyword(s):  
High Resolution ◽  
Video Coding ◽  
Memory Compression

Performance analysis, parameter selection and extensions to H.264/AVC FRExt for high resolution video coding

2011 ◽  
Vol 22 (8) ◽  
pp. 749-759 ◽  
Author(s):  
Chenwei Deng ◽  
Weisi Lin ◽  
Bu-sung Lee ◽  
Chiew Tong Lau ◽  
Ming-Ting Sun
Keyword(s):  
High Resolution ◽  
Performance Analysis ◽  
Video Coding ◽  
Parameter Selection

Tunneling High-Resolution Color Content through 4:2:0 HEVC and AVC Video Coding Systems

2013 ◽  
Author(s):  
Yongjun Wu ◽  
S. Kanumuri ◽  
Yifu Zhang ◽  
S. Sadhwani ◽  
G. J. Sullivan ◽  
...  
Keyword(s):  
High Resolution ◽  
Video Coding ◽  
Coding Systems

scholarly journals A Cloud-Based Distributed Architecture to Accelerate Video Encoders

2020 ◽  
Vol 10 (15) ◽  
pp. 5070
Author(s):  
Juan Gutiérrez-Aguado ◽  
Raúl Peña-Ortiz ◽  
Miguel Garcia-Pineda ◽  
Jose M. Claver
Keyword(s):  
High Resolution ◽  
Video Coding ◽  
Signal To Noise Ratio ◽  
Elderly Care ◽  
Video Quality ◽  
Parallel Execution ◽  
Distributed Architecture ◽  
High Definition ◽  
Self Driving Cars ◽  
Cloud Infrastructures

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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