High-Resolution Video Processing With Adaptive Rate-Distortion Scaling
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Solution Overview
Problem
Existing video compression techniques struggle to achieve high compression rates while maintaining video quality, particularly with increasing video resolutions.
Innovation Solution
A method and apparatus for pre-processing video content involves generating groups of video frames with varying resolutions and distortion scores, followed by selecting an optimal group based on rate-distortion comparison, and post-processing to reduce compression artifacts using convolutional neural networks (CNNs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video compression rate is increased to reduce storage and bandwidth requirements, then storage and transmission efficiency is improved, but video quality deteriorates due to loss of detail and increased compression artifacts
Solution Approach 1:
The system performs preliminary analysis of video content characteristics (complexity, motion, texture) before compression to determine optimal compression parameters. This advance preparation allows the encoder to apply different compression strategies to different video regions and segments, preserving quality where needed while maximizing compression where possible.
Solution Approach 2:
The patent applies different compression techniques and quality levels to different regions of video frames based on local characteristics. Important regions (faces, text, motion-critical areas) receive higher quality treatment while less important regions are compressed more aggressively. This regional differentiation resolves the contradiction by maintaining overall quality while achieving high average compression.
2Manufacturing precision
If video resolution is increased to improve picture quality, then visual fidelity is improved, but data size and storage requirements increase significantly
Solution Approach 1:
The system dynamically adjusts video resolution and quality parameters based on content characteristics and playback conditions. Rather than using a fixed high resolution for all content, the resolution is adaptively modified - using higher resolutions for complex, detail-critical scenes and lower resolutions for simpler content, thereby maintaining quality while reducing overall data size.
Solution Approach 2:
The patent employs multiple resolution versions and quality parameters for the same video content. The system selects and switches between different resolution parameters (e.g., 4K, 1080p, 720p) based on content complexity, device capabilities, and network conditions, allowing high quality where needed while minimizing data storage requirements through parameter variation.
Data Source
AI summary
The present disclosure refers to methods and apparatuses for processing of high-resolution video content. In an embodiment, a method includes generating a first group of video frames from the video content. The first group of video frames has a first resolution lower than a resolution of the video content and a first rate-distortion score. The method further includes generating a second group of video frames from the video content. The second group of video frames has a second resolution lower than the resolution of the video content and a second rate-distortion score. The method further includes selecting an optimal group of video frames from the first and second groups of video frames based on a comparison between the first and second rate-distortion scores. The optimal group of video frames has a rate-distortion score lower than the first and the second rate-distortion scores.