Paper Info
Title
Complexity Modeling of the Motion Compensation Process of the H.264/AVC Video Coding Standard
Abstract
With recent advances in computing and communication technologies, ubiquitous access to high quality multimedia content such as high definition video using smart phones, Net books, or tablets is a fact of our daily life. However, power is still a major concern for any mobile device, and requires optimization of power consumption using a power model for each multimedia application, such as a video decoder. In this paper, a generic decoding complexity model for the motion compensation (MC) process, which constitutes up to 25% of the computational complexity and hence power consumption of an H.264/AVC decoder, has been proposed. For the model to remain independent from a specific implementation or platform, it has been developed by analysing the MC algorithm as described in the standard. Simulation results indicate that the proposed model estimates MC complexity with an average accuracy of 95.63%, for a wide range of test sequences using both JM and x.264 software implementations of H.264/AVC. For a dedicated hardware implementation of the MC module the modeling accuracy is around 89.61%, according to our simulation results. It should be noted that in addition to power consumption control, the proposed model can be used for designing a receiver-aware H.264/AVC encoder, where the complexity constraints of the receiver side are taken into account during compression.
Year
DOI
Venue
2012
10.1109/ICME.2012.91
ICME
Keywords
Field
DocType
power model,motion compensation process,generic decoding complexity model,mc algorithm,complexity modeling,simulation result,power consumption,complexity constraint,mc complexity,mc module,power consumption control,avc video coding standard,computational complexity,data compression,motion compensation,communication technology,interpolation,computational modeling,computing technology,mobile device,decoding
High-definition video,Computer science,Motion compensation,Coding (social sciences),Real-time computing,Artificial intelligence,Computer engineering,Video decoder,Computer vision,Encoder,Decoding methods,Data compression,Computational complexity theory
Conference
Citations 
PageRank 
References 
7
0.60
7
Authors
4
Name
Order
Citations
PageRank
Mehdi Semsarzadeh1536.23
Mohsen Jamali2125142.91
Mahmoud Reza Hashemi313127.70
Shervin Shirmohammadi41066125.81