Neural Network Video Artifact Removal via Residual Upsampling
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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 implementation of a convolutional neural network (CNN) based image enhancement service that uses generative adversarial networks (GANs) to remove compression artifacts and increase video image resolution, operating on streaming, stored, or static images, by selecting appropriate CNN profiles based on environmental factors and available resources.
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 degrades with compression artifacts
Solution Approach 1:
The patent applies GANs to convert the harmful compression artifacts into beneficial enhanced image quality. The generator network learns to reconstruct high-quality images from compressed low-quality inputs, effectively transforming the degradation caused by compression into an opportunity for intelligent enhancement that removes artifacts while preserving bandwidth savings.
Solution Approach 2:
The system changes the parameter of image resolution by using super-resolution techniques within the GAN framework. The network transforms low-resolution compressed images into high-resolution enhanced images, effectively altering the spatial dimension parameter to improve image quality without increasing the original bandwidth requirements.
2Manufacturing precision
If image resolution is increased to improve quality, then image quality improves, but bandwidth requirements increase
Solution Approach 1:
The enhancement is performed preliminarily at the source or encoding stage rather than requiring transmission of high-resolution data. By applying the GAN-based enhancement to compressed low-resolution images, the system achieves high-quality output without the bandwidth cost of transmitting or processing high-resolution original data.
Solution Approach 2:
The system creates a high-quality copy of the compressed image through intelligent generation rather than direct transmission. The GAN generates a synthetic high-resolution version that copies the essential visual information while eliminating compression artifacts, achieving quality improvement without proportional bandwidth increase.
3Manufacturing precision
If complex enhancement algorithms are applied to remove artifacts, then image quality improves, but computational complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic artifact removal methods with a learned neural network model. Instead of using complex signal processing algorithms, the system uses a GAN that has been pre-trained to recognize and remove compression artifacts, shifting the computational burden to the training phase and enabling more efficient real-time enhancement.
Solution Approach 2:
The system changes the computational approach by using deep learning parameters (weights and biases of the neural network) instead of traditional algorithmic parameters. The GAN learns optimal enhancement parameters during training, allowing for efficient inference with reduced computational complexity compared to iterative optimization methods.
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 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 previously generated higher resolution image in the video file, upscaling the received image using bilinear upsampling, and combining the upscaled received image and residual value to generate an enhanced image.