Prediction Method for Image Coding Quality Based on Differential Information Entropy
AbstractFor the requirement of quality-based image coding, an approach to predict the quality of image coding based on differential information entropy is proposed. First of all, some typical prediction approaches are introduced, and then the differential information entropy is reviewed. Taking JPEG2000 as an example, the relationship between differential information entropy and the objective assessment indicator PSNR at a fixed compression ratio is established via data fitting, and the constraint for fitting is to minimize the average error. Next, the relationship among differential information entropy, compression ratio and PSNR at various compression ratios is constructed and this relationship is used as an indicator to predict the image coding quality. Finally, the proposed approach is compared with some traditional approaches. From the experiments, it can be seen that the differential information entropy has a better linear relationship with image coding quality than that with the image activity. Therefore, the conclusion can be reached that the proposed approach is capable of predicting image coding quality at low compression ratios with small errors, and can be widely applied in a variety of real-time space image coding systems for its simplicity.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Tian, X.; Li, T.; Tian, J.-W.; Li, S. Prediction Method for Image Coding Quality Based on Differential Information Entropy. Entropy 2014, 16, 990-1001.
Tian X, Li T, Tian J-W, Li S. Prediction Method for Image Coding Quality Based on Differential Information Entropy. Entropy. 2014; 16(2):990-1001.Chicago/Turabian Style
Tian, Xin; Li, Tao; Tian, Jin-Wen; Li, Song. 2014. "Prediction Method for Image Coding Quality Based on Differential Information Entropy." Entropy 16, no. 2: 990-1001.