Floating Image Quality Network for Multi-Resolution Processing
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Solution Overview
Problem
Existing image processing systems face structural performance limitations and efficiency decreases due to fixed positioning of image quality processing networks, which are inefficient for handling images of varying resolutions and characteristics, leading to increased costs and reduced image quality improvement.
Innovation Solution
An electronic device and method that dynamically adjusts the positioning of image quality processing networks based on input image resolution and characteristics, utilizing a floating-type network that identifies optimal domains and selects appropriate unit calculation modules through a convolution bank, enabling efficient processing across different resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed positioning image quality processing network is used, then the network structure is simple and easy to implement, but the system suffers from structural performance limitations and efficiency decreases when handling images of varying resolutions
Solution Approach 1:
The patent implements a floating-type image quality processing network that dynamically adjusts its positioning based on input image resolution. The network can float between different resolution domains (e.g., 2K domain, 4K domain, 8K domain) and automatically select the appropriate domain for processing. This dynamic positioning mechanism resolves the contradiction by enabling the system to adapt to varying image resolutions, thereby maintaining high processing efficiency without sacrificing implementation simplicity through the use of a unified network architecture that can operate in multiple domains.
Solution Approach 2:
The system changes the operational parameters of the image quality processing network by adjusting its positioning domain based on input image resolution characteristics. When a low-resolution image is input, the network positions itself in the 2K domain; for medium-resolution images, it moves to the 4K domain; and for high-resolution images, it operates in the 8K domain. This parameter change approach allows the network to optimize processing efficiency for different resolution inputs while maintaining a consistent structural implementation.
2Productivity
If the image quality processing network is positioned in a specific resolution domain, then the processing is efficient for that resolution, but the system lacks adaptability for handling images of different resolutions and characteristics
Solution Approach 1:
The patent creates a universal image quality processing network that can function across multiple resolution domains. The floating-type network is designed to operate in 2K, 4K, and 8K domains, making it multi-functional. A single network architecture serves multiple purposes by dynamically positioning itself in the appropriate domain based on input image resolution, thereby achieving both processing efficiency for specific resolutions and broad adaptability across different resolution types.
Solution Approach 2:
The network's positioning domain is made dynamic rather than static. The system continuously adjusts the network's operational domain based on the characteristics of the input image. This dynamic adaptation enables the network to maintain high processing efficiency for the current input resolution while simultaneously demonstrating versatility across different resolution types, as it can transition between domains as needed.
3Adaptability or versatility
If multiple fixed positioning networks are used for different resolutions, then the system can handle various resolutions effectively, but the device complexity and costs increase
Solution Approach 1:
The patent merges multiple resolution-specific processing capabilities into a single floating-type image quality processing network. Instead of implementing separate fixed networks for 2K, 4K, and 8K resolutions, the system combines these functionalities into one unified network that can operate in multiple domains. This merging approach reduces device complexity and costs while maintaining the ability to handle various resolutions effectively, as the single network dynamically positions itself in the appropriate domain based on input characteristics.
4Productivity
If a floating-type image quality processing network is implemented, then the system achieves high adaptability and efficiency for varying resolutions, but the network positioning and module selection complexity increases
Solution Approach 1:
The floating-type image quality processing network employs a self-service mechanism where the system automatically determines the appropriate positioning domain and selects the corresponding unit calculation modules based on input image resolution characteristics. The processor autonomously identifies whether the input image requires 2K, 4K, or 8K domain processing and configures the network accordingly without requiring complex external control mechanisms. This self-service approach manages the positioning complexity internally while maintaining high processing efficiency and adaptability.
Data Source
AI summary
An electronic device including: memory storing instructions; and a processor, wherein the instructions, when executed by the processor, cause the electronic device to: based on receiving an input image, identify an image-processing domain corresponding to an image quality processing network; identify, based on the identified image-processing domain, a unit calculation module for each of a plurality of layers in the image quality processing network; implement a floating-type image quality processing network by controlling first layer information to be input into the identified unit calculation module corresponding to a first layer among the plurality of layers, and inputting an output of the first layer into the identified unit calculation module corresponding to a second layer among the plurality of layers; and process the input image using the implemented floating-type image quality processing network, and wherein each of the plurality of image-processing domains corresponds to a different image resolution.