Name
Abstract | ||
|---|---|---|
This paper introduces a novel class of Graph-based Transform based on neural networks (GBT-NN) within the context of block-based predictive transform coding of imaging data. To reduce the signalling overhead required to reconstruct the data after transformation, the proposed GBT-NN predicts the graph information needed to compute the inverse transform via a neural network. Evaluation results on se... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MLSP52302.2021.9596317 | 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | ISSN |
Laplace equations,Symmetric matrices,Quantization (signal),Conferences,Transform coding,Transforms,Machine learning | Conference | 2161-0363 |
ISBN | Citations | PageRank |
978-1-7281-6338-3 | 1 | 0.37 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debaleena Roy | 1 | 5 | 1.83 |
| Tanaya Guha | 2 | 242 | 13.54 |
| Victor Sanchez | 3 | 144 | 31.22 |