Image Enhancement Feedback via Discriminator Networks
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Solution Overview
Problem
Current image and video enhancement methods lack a feedback mechanism to quantify and visualize the amount of enhancement performed, making it difficult for users to understand the quality improvements made to media objects.
Innovation Solution
An image enhancement system utilizing a discriminator network to assess media object quality and a generative neural network for enhancement, providing an enhancement score that quantifies the changes made, allowing for both visual and numerical feedback to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual image enhancement or automatic image editing software is used, then image quality can be improved, but users cannot see quantifiable feedback on the amount of enhancement performed
Solution Approach 1:
The patent implements a feedback mechanism where a discriminator network evaluates both the original and enhanced images to generate an enhancement score. This score quantifies the amount of enhancement performed, providing users with measurable feedback on the improvement made. The discriminator network compares features between original and enhanced images to calculate a numerical score that represents the enhancement magnitude, thus resolving the contradiction by preserving quantifiable information about the enhancement process.
2Manufacturing precision
If enhancement is performed on low-quality media objects, then image quality can be improved, but it is difficult to measure and visualize the enhancement amount
Solution Approach 1:
The patent introduces a discriminator network as an intermediary component that acts as a measurement tool between the enhancement process and the user. This discriminator network objectively evaluates the enhancement by comparing original and enhanced images, generating a numerical enhancement score that makes the enhancement amount detectable and measurable. The intermediary discriminator translates the complex visual changes into a simple, interpretable score, resolving the difficulty of measuring enhancement.
3Loss of information
If a feedback mechanism is added to quantify enhancement, then user understanding of quality improvement is enhanced, but system complexity increases
Solution Approach 1:
The patent makes the discriminator network multi-functional by using it for both image quality evaluation and enhancement scoring. The same discriminator that assesses whether an image is real or generated is also used to quantify the enhancement amount by comparing original and enhanced images. This universal use of the discriminator reduces overall system complexity compared to having separate components for quality assessment and enhancement measurement.
Data Source
AI summary
The present disclosure describes systems and methods for image enhancement. Embodiments of the present disclosure provide an image enhancement system with a feedback mechanism that provides quantifiable image enhancement information. An image enhancement system may include a discriminator network that determines the quality of the media object. In cases where the discriminator network determines that the media object has a low image quality score (e.g., an image quality score below a quality threshold), the image enhancement system may perform enhancement on the media object using an enhancement network (e.g., using an enhancement network that includes a generative neural network or a generative adversarial network (GAN) model). The discriminator network may then generate an enhancement score for the enhanced media object that may be provided to the user as a feedback mechanism (e.g., where the enhancement score generated by the discriminator network quantifies the enhancement performed by the enhancement network).