LMSDA Video Encoding for Lightweight Texture Recovery
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Solution Overview
Problem
Existing video encoding technologies face challenges in maintaining image quality during upsampling after downsampling, particularly in high-definition videos, and existing neural network-based methods either require excessive network parameters or fail to accurately recover lost textures.
Innovation Solution
The Lightweight Multi-level mixed Scale and Depth information with Attention mechanism (LMSDA) network employs residual learning and attention mechanisms to extract multi-scale and depth information, using shared convolutional layers to reduce parameters and enhance feature extraction, incorporating multi-scale spatial and channel attention blocks to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing neural network-based methods are used for upsampling, then image quality may be improved, but network parameters become excessive
Solution Approach 1:
The patent extracts and emphasizes only the essential components needed for effective upsampling: residual learning modules and attention mechanisms. By removing unnecessary network components and focusing on these key elements, the model achieves good image quality with fewer parameters, directly resolving the contradiction between image quality and network complexity
Solution Approach 2:
The patent changes the parameter configuration by using shared convolutional layers with different kernel sizes and adjusting the depth of network layers. This parameter optimization allows the network to achieve effective upsampling performance with reduced computational complexity, balancing image quality and model size
2Manufacturing precision
If existing neural network-based methods are used for upsampling, then image quality may be improved, but lost textures are not accurately recovered
Solution Approach 1:
The patent applies preliminary action through residual learning, where the network learns to recover residual information that was lost during downsampling. By pre-learning the relationship between original and downsampled images, the model can accurately reconstruct lost textures during upsampling, addressing the texture recovery problem
Solution Approach 2:
The patent incorporates attention mechanisms that provide feedback about important texture regions. The attention modules dynamically adjust the reconstruction process based on the importance of different spatial locations, ensuring that lost textures are accurately recovered while reducing artifacts, thus improving both image quality and texture fidelity
3Productivity
If downsampling is applied to video encoding, then encoding efficiency is improved, but image quality deteriorates during upsampling
Solution Approach 1:
The patent introduces an intermediary upsampling module that bridges the gap between compressed low-resolution data and required high-quality output. This intermediary component, based on residual learning and attention mechanisms, restores image quality that would otherwise be lost, allowing efficient downsampling during encoding while maintaining output quality
Solution Approach 2:
The patent optimizes the trade-off between encoding efficiency and image quality by adjusting the depth and configuration of upsampling network layers. By carefully selecting network parameters and using shared convolutional layers, the model achieves effective quality restoration with minimal computational overhead, enabling efficient encoding while maintaining high output quality
Data Source
AI summary
A method of video encoding is provided. The method may include receiving, by a head portion of a Lightweight Multi-level mixed Scale and Depth information with Attention mechanism (LMSDA) network, an input image. The method may include extracting, by the head portion of the LMSDA network, a first set of features from the input image. The method may include inputting, by a backbone portion of the LMSDA network, the first set of features through a plurality of LMSDA blocks (LMSDABs). The method may include generating, by the backbone portion of the LMSDA network, a second set of features based on an output of the LMSDABs. The method may include upsampling, by a reconstruction portion of the LMSDA network, the second set of features to generate an enhanced output image.