Midway Video Equalization

Abstract

This article presents an implementation of the 'Midway Equalization' method for videos. This technique allows us to modify the image histograms so that they present similar luminances. We propose two algorithms: the first one based on histogram inversion and the second one on the sorting of images intensities. The former computes the histograms and then finds the contrast change functions by convolving the inverse histograms with a Gaussian function. The latter starts by sorting the pixels of each image by intensity; the temporal signals, composed of all gray levels of the same rank, are then convolved with a Gaussian function. In this sorting method, the resulting histograms are more similar and homogeneous. Nevertheless, the histogram strategy is faster and provides good results in general. The algorithms include a 'dithering' option for reducing quantization artifacts. The whole implementation depends on a single parameter, the standard deviation, that is used for Gaussian convolutions. The experiments show several examples, including the quantization artifacts that appear in some situations and the benefits of dithering. We observe that these artifacts are usually more important in the histogram method. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instruction are included in the README.txt file of the archive.

Comparison of Motion Smoothing Strategies for Video Stabilization using Parametric Models

“…Scalespace theory is the subject of several recent books [53,33,19]. It has been proposed to stabilize the video gray scale distribution and to compensate flickering effects [11,50]. Several works have used parametric models between two successive frames in the video for the purpose of video indexing, camera motion characterization, or ego-motion classification, such as [5] or [26].…”