P.W.A.C. Biezen

Bio: P.W.A.C. Biezen is an academic researcher from Philips. The author has contributed to research in topics: Motion estimation & Estimator. The author has an hindex of 3, co-authored 5 publications receiving 666 citations.

True-motion estimation with 3-D recursive search block matching

Abstract: A new recursive block-matching motion estimation algorithm with only eight candidate vectors per block is presented. A fast convergence and a high accuracy, also in the vicinity of discontinuities in the velocity plane, was realized with such new techniques as bidirectional convergence and convergence accelerators. A new search strategy, asynchronous cyclic search, which allows a highly efficient implementation, is presented. A new block erosion postprocessing proposal further effectively eliminates block structures from the generated vector field. Measured with criteria relevant for the field rate conversion application, the new motion estimator is shown to have a superior performance over alternative algorithms, while its complexity is significantly less. >

An efficient true-motion estimator using candidate vectors from a parametric motion model

Abstract: Some efficient motion estimation algorithms select their output motion vector from a limited number of likely correct candidate, or prediction, vectors. In this paper, next to the known spatial and temporal prediction vectors, an additional and independent prediction is proposed. This candidate is generated with a parametric model describing the global motion in a previously estimated motion vector field. The proposal is elaborated as an addition to the three-dimensional (3-D) recursive search block-matching algorithm. The evaluation shows that a subpixel accurate, true-motion estimator results with a very low operations count.

An evolutionary architecture for motion-compensated 100 Hz television

Abstract: In this paper, recently developed algorithms for high quality motion-compensated up-conversion are combined in a new architecture closely resembling that of current 100 Hz consumer television sets. By merging the motion estimation and the motion compensation part, the estimated silicon area could be reduced to a level where the entire functionality can be realized with one processing chip replacing the currently used 100 Hz processing chip. This enables a simple evolution towards motion compensated 100 Hz TV, considered to be very attractive. The architectural choice, and the wish to share expensive memories, requires some modifications in the motion estimator and up-convertor design which are discussed. The specific case of movie programs is dealt with, and it is also shown how the evolutionary architecture can achieve a significantly improved motion portrayal for this movie material. >

Picture signal processing mode control

Abstract: In a method of controlling a picture signal processing mode, first and second motion vectors are determined (ME, D T ) for first and second fields, and a picture signal processing mode control signal is obtained by comparing (D T , -, COMP-R) the first and second motion vectors, in order to determine whether the motion vectors are reliable enough to allow for a motion-compensated picture signal processing.

Apparatus for motion-compensated picture signal interpolation

Abstract: of EP0574068In a motion-compensated picture signal interpolation apparatus comprising a field memory (FM2) for delaying an input field sequence, a motion vector estimator (ME) for furnishing motion vectors, and a motion-compensated interpolator (MC) coupled to an input and an output of the field memory (FM2) for furnishing a sequence of motion-compensated output fields on the basis of the motion vectors, inputs of the motion vector estimator (ME) are coupled to the input and the output of the field memory (FM2) to estimate the motion vectors by means of the same single field memory (FM2) which is used for the motion-compensated interpolation.

Motion-Compensated Frame Interpolation Using Bilateral Motion Estimation and Adaptive Overlapped Block Motion Compensation

Abstract: In this work, we develop a new motion-compe (MC) interpolation algorithm to enhance the temporal resolution of video sequences. First, we propose the bilateral motion estimation scheme to obtain the motion field of an interpolated frame without yielding the hole and overlapping problems. Then, we partition a frame into several object regions by clustering motion vectors. We apply the variable-size block MC (VS-BMC) algorithm to object boundaries in order to reconstruct edge information with a higher quality. Finally, we use the adaptive overlapped block MC (OBMC), which adjusts the coefficients of overlapped windows based on the reliabilities of neighboring motion vectors. The adaptive OBMC (AOBMC) can overcome the limitations of the conventional OBMC, such as over-smoothing and poor de-blocking. Experimental results show that the proposed algorithm provides a better image quality than conventional methods both objectively and subjectively

New frame rate up-conversion using bi-directional motion estimation

Abstract: We propose a new frame rate up-conversion algorithm for high quality video. In the proposed scheme, bi-directional motion estimation (ME) is performed to construct the motion vector (MV) field for the frame to be interpolated. Unlike conventional motion-compensated interpolation (MCI) algorithms, the proposed technique does not produce any overlapped pixel and hole region in the interpolated frame, and thus can utilize the overlapped block motion compensation technique to reduce the blocking artifacts. The proposed algorithm is very simple to implement on consumer products when compared to conventional MCI methods. Computer simulation shows a high visual performance of the proposed frame rate up-conversion algorithm.

Motion estimation and motion-compensated interpolation

Abstract: In a method of estimating motion, at least two motion parameter sets are generated (PE1-PEn) from input video data (n, n-1), a motion parameter set being a set of parameters describing motion in an image, by means of which motion parameter set motion vectors can be calculated. One motion parameter set indicates a zero velocity for all image parts in an image, and each motion parameter set has corresponding local match errors. Output motion data are determined from the input video data (n, n-1) in dependence on the at least two motion parameter sets, wherein the importance of each motion parameter set in calculating the output motion data depends on the motion parameter sets' local match errors.

MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

Abstract: Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize target frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video frame interpolation and enhancement algorithms on a wide range of datasets.

MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

Abstract: Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize target frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net architecture can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video frame interpolation and enhancement algorithms on a wide range of datasets.