In this paper, a new low-complexity true-motion estimation (TME) algorithm is proposed for video processing applications, such as motion-compensated temporal frame interpolation (MCTFI) or motion-compensated frame rate up-conversion (MCFRUC). Regular motion estimation, which is often used in video coding, aims to find the motion vectors (MVs) to reduce the temporal redundancy, whereas TME aims to track the projected object motion as closely as possible. TME is obtained by imposing implicit and/or explicit smoothness constraints on the block-matching algorithm. To produce better quality-interpolated frames, the dense motion field at interpolation time is obtained for both forward and backward MVs; then, bidirectional motion compensation using forward and backward MVs is applied by mixing both elegantly. Finally, the performance of the proposed algorithm for MCTFI is demonstrated against recently proposed methods and smoothness constraint optical flow employed by a professional video production suite. Experimental results show that the quality of the interpolated frames using the proposed method is better when compared with the MCFRUC techniques.

In this letter, a new fast motion estimation (FME) algorithm capable of producing sub-sample motion vectors at low computational-complexity is proposed. Unlike existing FME algorithms, the proposed algorithm considers the low complexity sub-sample accuracy in designing the search pattern for FME. The proposed FME algorithm is designed in such a way that the block distortion measure is modeled as a parametric surface in the vicinity of the integer-sample motion vector; this modeling enables low computational-complexity sub-sample motion estimation (ME) without pixel interpolation. Experimental results on video test sequences show that the proposed FME algorithm reduces computational complexity of integer and sub-sample ME considerably compared with traditional methods at the cost of negligible performance degradation.

A general framework based on histogram equalization for image contrast enhancement is presented. In this framework, contrast enhancement is posed as an optimization problem that minimizes a cost function. Histogram equalization is an effective technique for contrast enhancement. However, a conventional histogram equalization (HE) usually results in excessive contrast enhancement, which in turn gives the processed image an unnatural look and creates visual artifacts. By introducing specifically designed penalty terms, the level of contrast enhancement can be adjusted; noise robustness, white/black stretching and mean-brightness preservation may easily be incorporated into the optimization. Analytic solutions for some of the important criteria are presented. Finally, a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.

Local contrast enhancement (LCE) gives a more lively look to an image or video. With a larger difference of a pixel's luma value from its local mean, the eye needs less time to adjust to that local region for a better contrast sensitivity. Since the human eye is trained and conditioned to recognize a natural looking face, the faces in a locally enhanced image may look unnatural even though other parts of the enhanced image is visually more attractive than the original image. To solve this problem, a low-complexity skin-aware local contrast enhancement algorithm is proposed. The proposed skin-aware local contrast enhancement (SALSA) algorithm avoids creating false edges. Experiment results show that SALSA produces natural looking face and non-facial skin regions in the locally enhanced image.

Edges that are visible in color images may not be detected in the corresponding grayscale image. This is due to the neighboring objects having different hues but the same intensities. Hence, a color edge preserving grayscale conversion algorithm is proposed that helps detect color edges using only the luminance component. The algorithm calculates an approximation to the first principal component to form a new set of luminance coefficients instead of using the conventional luminance coefficients. This method can be directly applied to all existing grayscale edge detectors for color edge detection. Processing only one channel instead of three channels results in lower computational complexity compared to other color edge detectors. Experimental results on test images show similar edge detection capabilities to typical color edge detectors at reduced complexity levels.

An unsharp masking type local contrast enhancement method that uses spatially adaptive 2-dimensional (2-D) recursive filters is presented. The proposed 2-D recursive filters are a family of exponential smoothing filters. Three different forms are proposed: separable extension of the 1-dimensional (1-D) case, non-separable filter that uses 4-neighbors, and non-separable filter that uses 8-neighbors. The isotropy of the transfer function improves when moved from the first to the last; hence, better visual quality is obtained in the real video experiments. The computational complexity of the proposed recursive filters, unlike non-recursive filters, is independent of the desired cutoff frequency. This independence enables different levels of smoothing simply by spatially adapting the recursion coefficients without any additional computation