Real-Time Video Super-Resolution via Adaptive Frame Transfer
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Solution Overview
Problem
Current super-resolution algorithms for up-sampling videos to higher resolutions are computationally expensive and power-intensive, making them unsuitable for real-time application on devices like televisions and mobile screens, especially when dealing with high-definition and 4K Ultra-HD content, due to their high computation cost and energy consumption.
Innovation Solution
The FAST (Free Adaptive Super-resolution via Transfer) method leverages inter-frame similarity in compressed videos to transfer super-resolution results from one frame to another, applying the computationally intensive process only to a subset of frames, using embedded video compression parameters to reduce processing load and achieve real-time super-resolution with negligible runtime and computation cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If super-resolution algorithms are applied to up-sample videos to higher resolution, then visual quality is improved, but computation cost and power consumption increase significantly
Solution Approach 1:
The patent applies super-resolution processing in advance to selected key frames (I-frames and P-frames) before video playback. The enhanced frames are then stored and reused during playback, avoiding the need to apply computationally intensive SR algorithms to every frame in real-time. This preliminary processing approach allows high-quality SR results to be achieved while significantly reducing real-time computation requirements and power consumption.
Solution Approach 2:
The patent segments the video processing task by applying super-resolution selectively to specific frame types (I-frames and P-frames) rather than all frames. This segmentation strategy divides the computationally intensive SR operation into discrete, manageable units that can be processed in advance and reused, thereby reducing overall computation cost and power consumption while maintaining visual quality.
2Manufacturing precision
If super-resolution algorithms are applied to up-sample videos to higher resolution, then visual quality is improved, but processing speed decreases
Solution Approach 1:
The patent performs super-resolution processing on key frames before video playback in advance mode. The enhanced frames are cached and reused during playback, enabling real-time or near-real-time video display with high visual quality without the computational bottleneck of applying SR to every frame. This approach dramatically improves processing speed for real-time applications while maintaining SR quality.
Solution Approach 2:
The patent segments video frames into different types (I-frames, P-frames, B-frames) and applies super-resolution selectively to I-frames and P-frames while using motion compensation for B-frames. This segmentation enables the system to achieve high processing speeds by avoiding SR processing on frames that can be efficiently reconstructed through motion compensation, thereby maintaining productivity while improving visual quality.
3Productivity
If simple interpolation and filtering are used for up-sampling, then processing speed is maintained, but visual quality deteriorates
Solution Approach 1:
The patent applies computationally intensive super-resolution algorithms in advance to key frames, storing the high-quality results for reuse during playback. This preliminary SR processing eliminates the need to use simple interpolation during real-time playback, thereby achieving both high visual quality and maintained processing speed without the trade-off present in conventional approaches.
Solution Approach 2:
The patent combines super-resolution processing with motion compensation techniques. By merging SR results with motion-compensated frame prediction, the system achieves high visual quality while maintaining real-time processing capabilities during playback, as the computationally intensive SR operation is performed only on key frames rather than all frames.
4Manufacturing precision
If state-of-the-art neural network based SR algorithms are used, then visual quality is improved, but device complexity and power requirements increase
Solution Approach 1:
The patent performs neural network-based super-resolution processing in advance on key frames, storing the enhanced results for playback. This approach allows the use of complex, high-quality SR algorithms without requiring the device to have continuous access to powerful GPUs during playback. The complexity is shifted to an offline or pre-processing stage, making state-of-the-art SR algorithms accessible on resource-constrained devices.
Solution Approach 2:
The patent segments the application of complex neural network SR algorithms to only key frames (I-frames and P-frames) rather than all video frames. This segmentation reduces the overall computational burden and device complexity requirements, as the expensive SR processing is applied selectively rather than continuously, enabling high visual quality on devices with limited processing capabilities.
Data Source
AI summary
A real-time system and method for displaying video on a display are disclosed. Received compressed video data is decoded to produce an uncompressed first video frame, a first frame syntax element, an uncompressed second video frame, and a second frame syntax element. A computationally intensive process is applied to the uncompressed first video frame to produce an enhanced first video frame. A block having a portion of the enhanced first video frame from the enhanced first video frame is adaptively transferred to the uncompressed second video frame to produce an enhanced second video frame without applying the computationally intensive process to the uncompressed second video frame. The transferring is guided by the first frame syntax element and the second frame syntax element. The enhanced first video frame and the enhanced second video frame are displayed.