Effects of Color Systems' Transformation on Optical Flow Estimation of Noisy and Degraded Images

Varying illumination and image blur are some of major challenges faced by contemporary methods of optical flow estimation. Despite significant advancement, these aspects have not received much of attention by modern-day methods. Latest work in this field is heavily affected and produce adverse results when dealing with images containing variable illumination and blur. In this paper, we investigate the effects of color space transformations on optical flow estimation from degraded and noisy images. In our experiments, clean and noisy images have been used. These images contain different kinds of blur and atmospheric effects such as fog, mist, shadows and dark regions. By estimating optical flow with three types of sequences in parallel (super clean, clean and noisy), and using four popular color systems, the effects of color space transformation have been observed on the estimated flow fields. The four color systems include RGB (red, blue, green), HSV (hue, saturation, value), HSL (hue, saturation, lightness) and XYZ (as standardized by the International Commission on Illumination in 1931). It is found that output of an optical flow algorithm not only depends on the color system adopted, but some color spaces tend to favor some special type of image sequences. For instance, XYZ color system is more favorable for the images abiding by the brightness constancy assumption while HSV color space is more suitable for blurry and noisy images. While keeping rest of the parameters unchanged but only transforming the color-space, we estimated the optical flow. Obviously the results of an algorithm applied to clean images for optical flow, would not be consistent with a flow estimated from same images containing noise. The objective is to compare this adversative effect for different color spaces. The flow estimation errors in four color systems have been reported and compared, and the best color-space is pointed out in each case. The paper also discusses the possible factors behind these variable outcomes with an insight into the basic frameworks of traditional methods for optical flow.