Fractal Decoded Image Quality Prediction

摘要: To predict fractal decoded image quality more efficiently, an effective decoded image quality prediction method was proposed in this study. In fractal encoding process, the dynamic range of the linear correlation coefficients (LCCs) between range blocks and their best-matched domain blocks was greatly extended by several outliers which increased uncertainty and resulted in reduced prediction accuracy. To remove the interference of outliers, we introduced the effective minimum and maximum of LCCs, which provided the effective bottom and top limits of the actual percentage of accumulated collage error (EBL-APACE and ETL-APACE), respectively. Further, when EBL-APACE reached a large percentage, the average collage error (ACER) can be estimated, and the decoded image quality can be predicted directly.
Experimental results show that compared with the previous method, the proposed method can provide higher prediction accuracy with fewer computations.