Neural Multi-Layer Video Coding for Low Bit-Rate 8K Streaming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demand for high-resolution video content, such as Ultra-HD and 8K, necessitates a reduction in video transmission bit-rate without compromising visual presentation quality, while also managing computational complexity and facilitating a seamless transition between various video codecs.
Innovation Solution
A content-adaptive multi-layer coding approach using neural networks for downscaling and upscaling video content, optimizing encoding parameters based on content type and characteristics, and employing pre-trained neural networks to minimize residual signals, enabling efficient encoding and decoding across different video codecs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video resolution is increased to Ultra-HD or 8K, then visual presentation quality is improved, but bandwidth requirements increase significantly
Solution Approach 1:
The patent segments video content into multiple layers with different resolutions and quality levels. Each layer represents a different version of the video content, allowing the system to transmit only the necessary layers based on network conditions and device capabilities, thereby reducing overall bandwidth requirements while maintaining visual quality where needed.
Solution Approach 2:
The patent dynamically changes encoding parameters such as resolution, bit-rate, and quality levels based on content characteristics and transmission conditions. By adjusting these parameters adaptively, the system optimizes the balance between visual presentation quality and bandwidth consumption for different video segments.
2Loss of energy
If bit-rate is reduced to decrease bandwidth usage, then bandwidth efficiency is improved, but visual presentation quality deteriorates
Solution Approach 1:
The patent applies different quality levels and compression ratios to different regions or segments of video content based on their importance and characteristics. Critical visual areas maintain higher quality with less compression, while less important areas use higher compression, thereby reducing overall bandwidth usage while preserving essential visual presentation quality.
3Productivity
If neural networks are used for content-adaptive encoding, then coding efficiency is improved, but computational complexity increases
Solution Approach 1:
The patent performs content analysis and encoding parameter optimization using neural networks during the offline preprocessing stage. By conducting computationally intensive neural network analysis beforehand, the system generates optimized encoding parameters that can be applied during actual video transmission without requiring real-time computational resources, thus reducing online computational complexity while maintaining high coding efficiency.
4Adaptability or versatility
If multi-layer coding is implemented to support multiple resolutions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal multi-layer video format that can be decoded by various devices with different capabilities. Each layer is designed to be independently decodable, allowing devices to select and decode only the layers they support, thereby achieving wide compatibility across different video codecs and device types without requiring complex encoding infrastructure.
Data Source
AI summary
Methods, systems, and apparatuses are described for encoding video. Video content to be encoded and sent to a computing device may be downscaled into one or more layers. The one or more layers may represent one or more versions of the video content such as one or more versions encoded at different resolutions. The residuals between each layer and the base layer may be upscaled so that one or more parameters associated with optimizing the encoding of the one or more layers may be determined by one or more neural networks based on the downscaling and upscaling process. The residuals between each layer, the one or more parameters, and the base layer may be encoded and sent to a computing device for decoding and playback of the video content using any of the versions of the video content.