Name
Abstract | ||
|---|---|---|
An active research topic in recent years is to design tone mapping operators (TMOs) that convert high dynamic range (H-DR) to low dynamic range (LDR) images, so that HDR images can be visualized on standard displays. Nevertheless, most existing work has been done in the absence of a well-established and subject-validated image quality assessment (IQA) model, without which fair comparisons and further improvement are difficult. Recently, a tone mapped image quality index (TMQI) was proposed, which has shown to have good correlation with subjective evaluations of tone mapped images. Here we propose a substantially different approach to design TMO, where instead of using any pre-defined systematic computational structure (such as image transformation or contrast/edge enhancement) for tone mapping, we navigate in the space of all images, searching for the image that optimizes TMQI. The navigation involves an iterative process that alternately improves the structural fidelity and statistical naturalness of the resulting image, which are the two fundamental building blocks in TMQI. Experiments demonstrate the superior performance of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICME.2014.6890304 | Multimedia and Expo |
Keywords | DocType | ISSN |
data visualisation,image capture,iterative methods,optimisation,statistical analysis,HDR image visualization,IQA model,TMO,TMQI,high dynamic range image tone mapping,image quality assessment,iterative process,standard displays,statistical naturalness,structural fidelity,subjective evaluation,tone mapped image quality index optimization,tone mapping operator,high dynamic range imaging,image quality assessment,statistical naturalness,structural fidelity,tone mapped image quality index (TMQI),tone mapping | Conference | 1945-7871 |
Citations | PageRank | References |
10 | 0.55 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kede Ma | 1 | 773 | 27.93 |
| Hojatollah Yeganeh | 2 | 208 | 9.21 |
| Kai Zeng | 3 | 95 | 5.86 |
| Z Wang | 4 | 13331 | 630.91 |