Bridging Visual Gaps: AI Video Frame Interpolation Explained

The video editing industry is undergoing rapid digital transformations. In this innovative landscape, AI video frame interpolation has taken center stage. This article explores the intricacies of AI Video Frame Interpolation by outlining its operations, highlighting key aspects of the Tencent AI technique, discussing real-world applications, and envisioning its future potential. Let's start this in-depth understanding of this groundbreaking technology now!

What is AI Video Frame Interpolation?

AI Video Frame Interpolation is an advanced computational technique used in the domain of video processing. Generally, videos are composed of a series of frames that create the perception of motion when displayed at a rapid rate. However, due to limitations in recording technology or post-production editing, a video can often appear jerky or choppy if there are not enough frames per second. AI Video Frame Interpolation addresses this issue by generating new intermediate frames to enhance video fluidity and quality.

This technique leverages advancements in Artificial Intelligence (AI) and machine learning where AI models are trained to predict how objects in a scene will move and how the pixels will shift from one frame to the next. It interpolates these new frames based on the existing frames, leading to smoother videos with higher frame rates. This technology is especially valuable in slow-motion video production, video gaming, virtual reality, and other applications where fluid motion contributes significantly to the user experience.

How AI Video Frame Interpolation Works

The algorithmic functioning of AI video frame interpolation lies on two fundamental processes, namely motion estimation and motion compensation. During motion estimation, the AI model identifies and analyzes the movement of different pixels between consecutive frames. It determines the overall motion of the scene as well as the motion trajectories of different objects.

Once motion estimation is completed, the AI uses motion compensation to generate new frames. Here, pixels are effectively shifted according to the results of motion estimation. Then, the AI algorithm predicts the appearance of extra frames that should exist between the original frames. Leveraging deep learning techniques, the AI interpolates pixel values based on adjoining frames and creates an "in-between" frame that heightens the continuity and flow without a noticeable increase in the video's speed.

Key Features of Tencent AI Video Frame Interpolation

• A Smart Transcoding: Smart Transcoder technology intelligently analyzes the input video to optimize algorithms that improve subjective quality and bitrate. It includes features such as SDR-to-HDR upconversion, AI frame rate upsampling, Super Resolution upsampling, detail enhancement, color enhancement, and more. These features enhance videos in various scenarios like museums, archives, and scenic parks, providing a richer visual experience.
• Video Frame Insertion: This application uses AI deep learning models and optical flow estimation to generate new frames between existing ones, creating clear and fluid motion that looks more lifelike and immerses the viewer. It is helpful in modern adaptation of low frame rate videos, broadcasting/streaming media platforms, and old films restoration.
• Super Resolution: It is a video resolution upscaling technique that improves the subjective image quality and video resolution while intelligently preserving important details. It is suitable for museums, archives, and Scenic/Park large screens.
• Smart Enhancement: Integrating multiple video and image processing modules, it enhances the video quality and details, providing a richer experience with less processing time. Applicable scenarios include museums, archives, scenic parks, and old photo or cover image repair.
• Night Scene Enhancement: It intelligently improves brightness and object details in night scenes, alleviating the need for expensive equipment during night shooting. Suitable scenarios include parks with night tours.
• Detail Enhancement (Sharpening): Enhancing images with richer details, edge enhancement, and visual clarity, it can be applied to almost all videos to improve subjective picture quality.
• Smart Deblocking (Compression Distortion Repair): AI models trained to recognize and repair compression artifacts such as macroblocking and ringing effects, improving visual quality especially for overly compressed or transcoded videos.