Modeling the Pairwise Disparities in High Density Camera Arrays
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2018 26th European Signal Processing Conference (EUSIPCO) |
| Publisher | IEEE |
| Pages | 221-225 |
| ISBN (Electronic) | 978-9-0827-9701-5 |
| ISBN (Print) | 978-1-5386-3736-4 |
| DOIs | |
| Publication status | Published - Sep 2018 |
| Publication type | A4 Article in a conference publication |
| Event | European Signal Processing Conference - Duration: 1 Jan 1900 → … |
Publication series
| Name | |
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| ISSN (Electronic) | 2076-1465 |
Conference
| Conference | European Signal Processing Conference |
|---|---|
| Period | 1/01/00 → … |
Abstract
We discuss in this paper models for the disparity information needed when pairwise warping the angular views in a light field data set formed of N views. In one scenario of light field data compression, first a set of M reference views is encoded and then each of the remaining views is predicted by warping several reference views using disparity information. The necessary disparity information in this case may be as high as M(N-1) pairwise view disparity maps, estimated and transmitted independently for each pair (reference, target). We propose an estimation model which can be used in a flexible way for any selected configuration of references and predicted views. We study the estimation of the global model from the matching information provided by a pairwise matching program. The model may be defined in several ways, by considering the vertical and horizontal matches at various views and by allowing different model parameters for the regions from a segmentation of the scene. The regions based model is shown to perform better than a single region model. The performance of the model in synthesizing the unseen color views at specified locations in the views array is presented for several configurations of the estimation and prediction sets.