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

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

The present invention discloses methods of digitally converting 2D motion pictures or any other 2D image sequences to stereoscopic 3D image data for 3D exhibition. In one embodiment, various types of image data cues can be collected from 2D source images by various methods and then used for producing two distinct stereoscopic 3D views. Embodiments of the disclosed methods can be implemented within a highly efficient system comprising both software and computing hardware. The architectural model of some embodiments of the system is equally applicable to a wide range of conversion, re-mastering and visual enhancement applications for motion pictures and other image sequences, including converting a 2D motion picture or a 2D image sequence to 3D, re-mastering a motion picture or a video sequence to a different frame rate, enhancing the quality of a motion picture or other image sequences, or other conversions that facilitate further improvement in visual image quality within a projector to produce the enhanced images.

Methods and systems for converting 2d motion pictures for stereoscopic 3d exhibition

Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets. The source code and pre-trained model are available at https://github.com/baowenbo/DAIN.

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Depth-Aware Video Frame Interpolation

Video frame interpolation aims to synthesize nonexistent frames in-between the original frames. While significant advances have been made from the recent deep convolutional neural networks, the quality of interpolation is often reduced due to large object motion or occlusion. In this work, we propose a video frame interpolation method which explicitly detects the occlusion by exploring the depth information. Specifically, we develop a depth-aware flow projection layer to synthesize intermediate flows that preferably sample closer objects than farther ones. In addition, we learn hierarchical features to gather contextual information from neighboring pixels. The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame. Our model is compact, efficient, and fully differentiable. Quantitative and qualitative results demonstrate that the proposed model performs favorably against state-of-the-art frame interpolation methods on a wide variety of datasets.

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

A frame interpolation algorithm for frame rate up-conversion of progressive image sequences is proposed. The algorithm is based on simple motion compensation and linear interpolation. A motion vector is searched for each pixel in the interpolated image and the resulting motion field is median filtered to remove inconsistent vectors. Averaging along the motion trajectory is used to produce the interpolated pixel values. The main novelty of the proposed method is the motion compensation algorithm which has been designed with low computational complexity as an important criterion. Subsampled blocks are used in block matching and the vector search range is constrained to the most likely motion vectors. Simulation results show that good visual quality has been obtained with moderate complexity. The algorithm has been designed mainly for 50 Hz to 75 Hz frame rate up-conversion with applications in a multimedia environment, but it can also be used in advanced television receivers to remove artifacts due to low scan rate.

P. Haavisto

Papers

A method for motion adaptive frame rate up-conversion