Automated Author ProfileJantana Panyavaraporn
Jantana Panyavaraporn
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The H.264/AVC standard adopts a robust error resilience tool at the encoder known as Flexible Macroblock Ordering (FMO). The main goal of this tool is to provide a macroblock-level interleaving tool to spread out consecutive burst errors in a frame. Past research proposed the use of macroblock-coded bit-count which acts as spatial information as indicator of macroblock importance, and uses a two-pass encoding process to generate the macroblock-address map. In this dissertation, we propose to use a distortion measure based on concealment error which acts as temporal information as an indicator for a choice of macroblock-address-map of each picture. To avoid the incurred delay and complexity computing of two-pass encoding, we also propose a one-pass encoding scheme to generate the macroblock-address maps. Furthermore, we present a framework that combines one-pass FMO map generation and error concealment algorithms to improve the video quality due to transmission errors. The one-pass FMO map generation is accomplished by using feedback in terms of spatial and temporal information to simulate spatial and temporal error concealment at the encoder. The choice of error concealment method at decoder is applied according to parameter derived from residual information obtained at the encoder during the map generation process. Our simulation results performed under slow and fast fading channel confirm that the proposed technique can reduce the number of undecodable macroblock up to 80.54% and PSNR improvement are up to 6.09 dB when compared with methods that don’t use FMO and uses a simple non-motion compensated error concealment.
Authors
- Jantana Panyavaraporn