Neural Network Video Enhancement Resolving Compression Artifacts
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Solution Overview
Problem
Streaming video often suffers from degraded image quality due to compression artifacts and low resolution, caused by environmental factors and network congestion, which existing technologies fail to effectively address.
Innovation Solution
The use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) for image enhancement, which access additional image content and apply residual values to upsample images, removing compression artifacts and improving resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video is compressed to reduce bandwidth, then bandwidth consumption is reduced, but image quality deteriorates due to compression artifacts
Solution Approach 1:
A neural network model acts as an intermediary between the compressed video input and the final high-quality output. The model includes a resolution enhancement module that upscales the image and an artifact removal module that eliminates compression artifacts, thereby mediating the trade-off between bandwidth reduction and image quality preservation
Solution Approach 2:
The patent replaces traditional mechanical image processing methods with a neural network-based system. The neural network learns to reconstruct high-quality images from compressed inputs by substituting conventional signal processing mechanisms with data-driven deep learning approaches that can effectively remove artifacts and enhance resolution
2Manufacturing precision
If compression artifacts are removed and resolution is enhanced, then image quality is improved, but processing complexity increases
Solution Approach 1:
The neural network model is segmented into distinct functional modules: a resolution enhancement module that handles upsampling and an artifact removal module that targets compression artifacts. This segmentation allows each module to specialize in specific tasks, improving overall efficiency while managing complexity through modular design
Solution Approach 2:
The model performs preliminary resolution enhancement before artifact removal, preparing the image data in an optimal state for subsequent processing. This preliminary action structures the data flow to maximize the effectiveness of each processing stage while minimizing redundant computations
Data Source
AI summary
Techniques for enhancing an image are described. For example, a lower-resolution image from a video file may be enhanced using a trained neural network applying the trained neural network on the lower-resolution image to remove artifacts by removing artifacts by generating, using a layer of the trained neural network, a residual value based on the proper subset of the received image and at least one corresponding image portion of a preceding lower resolution image in the video file and at least one corresponding image portion of a subsequent lower resolution image in the video file, upscale the lower-resolution image using bilinear upsampling, and combine the upscaled received image and residual value to generate an enhanced image.