This paper introduces a novel approach leveraging objective image quality assessment (IQA) metrics to optimize the outcomes of traditional bicubic (BIC) image interpolation and interpolated scan conversion algorithms. Specifically, feature selection through line chart data visualization and computing the IQA metrics scores are used to estimate the IQA-guided coefficient-k that up-dates the traditional BIC algorithm weighting function. The resulting optimized bicubic (OBIC) algorithm was subjectively and objectively evaluated using natural and ultrasound images. Results showed that the overall performance of the OBIC algorithm was equivalent to 92.22% of 180 occurrences when compared to the BIC algorithm, while it was 57.22% of 180 occurrences when compared to other algorithms. On top of that, the OBIC interpolated scan conversion algorithm generally produced crisper and better contrast cropped ultrasound sectored images than the BIC algorithm, as well as other interpolated scan conversion algorithms mentioned.
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