Focus and Blurriness Measure Using Reorganized DCT Coefficients for an Autofocus Application

In this paper, two metrics for measuring image sharpness are presented and used for an autofocus (AF) application. Both measures exploit reorganized discrete cosine transform (DCT) representation. The first metric is a focus measure, which involves optimal high- and middle-frequency coefficients to evaluate relative sharpness. It is robust to noise while remaining sensitive to the best focus position. A psychometric function-based metric is introduced to quantify the focus measure. The second metric is a no-reference blurriness metric, which is used to measure absolute blurriness. It first constructs multiscale DCT edge maps using directional energy information and then determines image blurriness by combining change information in edge structures with image contrast. This metric gives predictions that are closely correlated with subjective perceived scores and shows performance comparable with that of state-of-the-art methods, especially for noisy images. For noisy situations, the two metrics are adjusted adaptively according to the estimated noise level. To prevent the introduction of extra computational load, an efficient noise-level estimation algorithm based on median absolute deviation is presented. This algorithm exploits only the available reorganized DCT coefficients. With the focus and blurriness measures, an AF method for which the two metrics play an important role was developed. Because of their high-quality performance, the realized AF function is able to locate the best focus position swiftly and reliably.