Paper Info
Title
Complexity Reduction Method For High Efficiency Video Coding Encoding Based On Scene-Change Detection And Image Texture Information
Abstract
Recently, as demand for high-quality video and realistic media has increased, High Efficiency Video Coding has been standardized. However, High Efficiency Video Coding requires heavy cost in terms of computational complexity to achieve high coding efficiency, which causes problems in fast coding processing and real-time processing. In particular, High Efficiency Video Coding inter-coding has heavy computational complexity, and the High Efficiency Video Coding inter prediction uses reference pictures to improve coding efficiency. The reference pictures are typically signaled in two independent lists according to the display order, to be used for forward and backward prediction. If an event occurs in the input video, such as a scene change, the inter prediction performs unnecessary computations. Therefore, the reference picture list should be reconfigured to improve the inter prediction performance and reduce computational complexity. To address this problem, this article proposes a method to reduce computational complexity for fast High Efficiency Video Coding encoding using information such as scene changes obtained from the input video through preprocessing. Furthermore, reference picture lists are reconstructed by sorting the reference pictures by similarity to the current coded picture using Angular Second Moment, Contrast, Entropy, and Correlation, which are image texture parameters from the input video. Simulations are used to show that both the encoding time and coding efficiency could be improved simultaneously by applying the proposed algorithms.
Year
DOI
Venue
2019
10.1177/1550147719892562
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Keywords
Field
DocType
Reduction of encoding complexity, High Efficiency Video Coding, computational intelligence for video coding, reference picture list, gray level co-occurrence matrix
Computer vision,Change detection,Image texture,Computer science,Reduction (complexity),Coding (social sciences),Artificial intelligence,Encoding (memory),Distributed computing
Journal
Volume
Issue
ISSN
15
12
1550-1477
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Hong-rae Lee100.34
Eun-bin Ahn200.34
A. S. Kim3277.38
Kwang-deok Seo400.34