π
π
Old Age
An Efficient Coding Method for Coding Region-of-Interest Locations in AVS2
February 28, 2015 Β· Declared Dead Β· π 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mingliang Chen, Weiyao Lin, Xiaozhen Zheng
arXiv ID
1503.00118
Category
cs.MM: Multimedia
Citations
3
Venue
2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
Last Checked
3 months ago
Abstract
Region-of-Interest (ROI) location information in videos has many practical usages in video coding field, such as video content analysis and user experience improvement. Although ROI-based coding has been studied widely by many researchers to improve coding efficiency for video contents, the ROI location information itself is seldom coded in video bitstream. In this paper, we will introduce our proposed ROI location coding tool which has been adopted in surveillance profile of AVS2 video coding standard (surveillance profile). Our tool includes three schemes: direct-coding scheme, differential- coding scheme, and reconstructed-coding scheme. We will illustrate the details of these schemes, and perform analysis of their advantages and disadvantages, respectively.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted