Name
Title | ||
|---|---|---|
Learning-Based Parameter Prediction For Quality Control In Three-Dimensional Medical Image Compression |
Abstract | ||
|---|---|---|
Quality control is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In high efficiency video coding (HEVC), regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory results. In this paper we propose a learning-based parameter prediction scheme to achieve efficient quality control. Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based quality control methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1631/FITEE.2000234 | FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING |
Keywords | DocType | Volume |
Medical image compression, High efficiency video coding (HEVC), Quality control, Learning-based, TP391 | Journal | 22 |
Issue | ISSN | Citations |
9 | 2095-9184 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuxuan Hou | 1 | 1 | 0.71 |
| Zhong Ren | 2 | 227 | 12.88 |
| Yubo Tao | 3 | 109 | 22.51 |
| Wei Chen | 4 | 1193 | 92.00 |