Enhanced Quadratic Video Interpolation (2009.04642v1)

Abstract: With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (e.g. acceleration) and successfully models the estimation of interpolated flow. However, its produced intermediate frames still contain some unsatisfactory ghosting, artifacts and inaccurate motion, especially when large and complex motion occurs. In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model. In particular, we adopt a rectified quadratic flow prediction (RQFP) formulation with least squares method to estimate the motion more accurately. Complementary with image pixel-level blending, we introduce a residual contextual synthesis network (RCSN) to employ contextual information in high-dimensional feature space, which could help the model handle more complicated scenes and motion patterns. Moreover, to further boost the performance, we devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process. The proposed EQVI model won the first place in the AIM2020 Video Temporal Super-Resolution Challenge.

• The paper introduces an improved video frame interpolation method by utilizing a rectified quadratic flow prediction to enhance motion estimation.
• It incorporates a residual contextual synthesis network that fuses high-dimensional features to reduce ghosting and occlusion artifacts.
• The enhanced model achieves a notable 0.38dB PSNR improvement, setting a new performance benchmark in complex motion scenarios.

Enhanced Quadratic Video Interpolation: A Methodological Advancement

The paper "Enhanced Quadratic Video Interpolation" introduces an improved method for video frame interpolation, which is a significant concern in digital video processing. The paper leverages recent advancements in deep learning to address the ill-posed nature of video frame interpolation, which involves predicting intermediate frames in low frame-rate videos. Building on the Quadratic Video Interpolation (QVI) model, the Enhanced Quadratic Video Interpolation (EQVI) represents a significant step forward in producing high-quality interpolated frames, particularly in scenarios with complex and large motions.

Methodology Insights

The EQVI is an enhancement over the existing QVI method, addressing some of its limitations like ghosting artifacts and inaccurate motion estimation. The improvements introduced in EQVI can be clustered into three core components:

1. Rectified Quadratic Flow Prediction (RQFP): This model refines the QVI's high-order motion exploitation by adopting a least squares approach. RQFP utilizes optical flows between four input frames to estimate acceleration and velocity more accurately. This refinement helps in better capturing the motion characteristic between frames, thereby reducing interpolation errors, notably in dealing with more extensive and complex motion.
2. Residual Contextual Synthesis Network (RCSN): Aimed at improving the robustness of motion estimation, this network introduces an overview module that operates in a high-dimensional feature space to incorporate contextual information. It addresses challenges related to occlusion and inaccurate motion by leveraging pre-extracted features from ResNet-18, thus ensuring a more comprehensive understanding of scene dynamics.
3. Multi-Scale Fusion Network (MS-Fusion): This component acts as a kind of learnable augmentation to combine outputs at various resolutions for better interpolation results. It integrates results from different resolution levels and optimizes them through a fusion network to enhance the spatial fidelity of the interpolated frames.

Evaluation and Numerical Results

The efficacy of the EQVI model is substantiated by its performance in the AIM2020 Video Temporal Super-Resolution Challenge, where it achieved first place. Performance metrics such as PSNR and SSIM indicate a noticeable improvement over the baseline QVI and other contemporary methods like Sepconv and Super-SloMo. Specifically, EQVI achieves a significant PSNR improvement of 0.38dB over the original QVI model on standardized datasets like REDS_VTSR5, highlighting its advanced capability in producing clearer and artifact-free frames.

Implications and Future Directions

The EQVI's methodological advancements offer substantial contributions to both theoretical understanding and practical applications in video processing. The proposed method could predominantly impact areas that demand high-quality video enhancement, such as film post-production, real-time streaming services, and virtual reality environments.

For theoretical implications, the adoption of RQFP and RCSN in frame interpolation could spark further research into more sophisticated motion modeling and contextual data integration, potentially leading to novel algorithms that could learn motion dynamics more intuitively and accurately.

Looking ahead, potential developments could explore even more efficient models capable of running in real-time applications, ensuring the applicability of this research to a broader spectrum of user-facing scenarios. Additionally, integrating these advancements into existing video codecs might also improve the efficiency of video transmission and storage, a crucial consideration in the era of streaming media consumption.

In summary, the EQVI model represents a substantial advancement in video frame interpolation methodologies. Through its thoughtful integration of improved motion prediction and synthesis networks, it sets a new benchmark for future research and application in video enhancement technologies.