The Library
Graph-based transforms based on prediction inaccuracy modeling for pathology image coding
Tools
Tools
Tools
Roy, Debaleena and Sanchez Silva, Victor (2017) Graph-based transforms based on prediction inaccuracy modeling for pathology image coding. In: 2018 Data Compression Conference, Utah, USA, 27-30 Mar 2018. Published in: 2018 Data Compression Conference pp. 157-166. ISSN 2375-0359. doi:10.1109/DCC.2018.00024
|
PDF
WRAP-graph-based-transforms-based-prediction-inaccuracy-modeling-pathology-image-coding- SanchezSilva-2017.pdf - Accepted Version - Requires a PDF viewer. Download (1743Kb) | Preview |
Official URL: https://doi.org/10.1109/DCC.2018.00024
Abstract
Digital pathology images are multi giga-pixel color images that usually require large amounts of bandwidth to be transmitted and stored. Lossy compression using intra - prediction offers an attractive solution to reduce the storage and transmission requirements of these images. In this paper, we evaluate the performance of the Graph - based Transform (GBT) within the context of block - based predictive transform coding. To this end, we introduce a novel framework that eliminates the need to signal graph information to the decoder to recover the coefficients. This is accomplished by computing the GBT using predicted residual blocks, which are predicted by a modeling approach that employs only the reference samples and information about the prediction mode. Evaluation results on several pathology images, in terms of the energy preserved and MSE when a small percentage of the largest coefficients are used for reconstruction, show that the GBT can outperform the DST and DCT.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RC Internal medicine |
||||||
| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Data compression (Computer science), Diagnostic imaging, Pathology | ||||||
| Journal or Publication Title: | 2018 Data Compression Conference | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2375-0359 | ||||||
| Official Date: | 23 July 2017 | ||||||
| Dates: |
|
||||||
| Page Range: | pp. 157-166 | ||||||
| DOI: | 10.1109/DCC.2018.00024 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 2018 Data Compression Conference | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Utah, USA | ||||||
| Date(s) of Event: | 27-30 Mar 2018 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
