Block-based Motion Estimation Method Research Articles

Frame Rate Up-Conversion Using Linear Quadratic Motion Estimation and Trilateral Filtering Motion Smoothing

Frame rate up-conversion (FRUC) is a widely used technique to enhance the visual quality of low frame rate video presented on liquid crystal displays. One decisive factor for an effective FRUC algorithm is to obtain true motion vectors (MVs) for motion compensated interpolation. However, many current block-based motion estimation methods cannot always provide reliable MVs. In this paper, a new FRUC algorithm employing linear quadratic motion estimation and trilateral filtering motion smoothing is presented to estimate true MVs. The MV obtained by minimizing matching error is modeled as the measurement of true MV with noise. To remove the noise, the measured MV is updated with the corresponding predicted MV by using linear quadratic estimation. In motion smoothing stage, a trilateral filter which considers local smoothness, neighboring motion reliability as well as motion diversity at motion boundary, is used to further improve the accuracy and density of motion vector field (MVF). Experimental results demonstrate that the proposed algorithm outperforms the conventional FRUC algorithms with better objective and subjective image quality.

Tongue of Ionization Motion Estimation From Polar TEC Sequences

In this letter, a new approach to tracking the ionospheric-storm enhancements known as tongues of ionization (TOIs) is presented. In contrast to conventional model-based methods, the technique works by applying block-based motion estimation methods directly to ionospheric total electron content image sequences. Solutions to the particular problems posed by the low image resolution and the nonrigid motion of the TOIs are proposed. Experimental results for an image sequence covering the 2003 ldquoHalloween stormrdquo establish that the approach produces estimated motion fields that show a good correspondence with the observable motion when the interframe differences are not extreme.

Analytic Estimation of Subsample Spatial Shift Using the Phases of Multidimensional Analytic Signals

In this correspondence, a method of analytic subsample spatial shift estimation based on an a priori n-D signal model is proposed. The estimation uses the linear phases of n analytic signals defined with the multidimensional Hilbert transform. This estimation proposes: i) an analytic solution to the n-D shift estimation and ii) an estimation without processing complex cross-correlation function or cross-spectra between signals contrary to most phase shift estimators. The method provides better performance in estimating subsample shifts than two classical estimators, one using the maximum of cross-correlation function and the other seeking the zero of the complex correlation function phase. Two delay estimators using the in-phase and quadrature-phase components of signals are also compared to our estimator. Like most estimators using the complex signal phases, the estimator proposed herein presents the advantage of unaltered accuracy when low sampled signals are used. Moreover, we show that this method can be applied to motion tracking with ultrasound images. Thus, included in a block-based motion estimation method and tested with ultrasound data, this estimator provides an analytical solution to the translation estimation problem.

Motion estimation for video compression using Kalman filtering

Motion estimation plays an important role for the compression of video signals. This paper presents a new block-based motion estimation method using Kalman filtering. The new method utilizes the predicted motion and measured motion to obtain an optimal estimate of motion vector. The autoregressive models are employed to fit the motion correlation between neighboring blocks and then achieve predicted motion information. The measured motion information is obtained by the conventional block-based fast search schemes. Several algorithms based on either one- or two dimensional models using either nonadaptive or adaptive Kalman filters are developed. The analysis of computational complexity and the simulation results indicate that the proposed method achieves significant savings on computation along with smoother motion vector fields and similar picture quality, when compared to the conventional full search algorithm.