Image Processing Configuration Reducing Package Size via Shared Logic
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Solution Overview
Problem
Existing image processing methods require repetitive processing logic in configuration files for different effect editing types, leading to increased application package size and development workload.
Innovation Solution
An image processing method that reuses pre-processing and post-processing function nodes across different effect editing operations by storing configuration information separately from the algorithm model and processing logic, reducing data redundancy and improving development efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If configuration files store complete processing logic for each effect editing type, then the processing logic is self-contained and reliable, but the application package size increases and development workload increases
Solution Approach 1:
The patent segments the configuration file into multiple parts: effect type identification information and processing logic identification information. The processing logic is stored separately in a shared library rather than being embedded in each configuration file. This segmentation allows the system to maintain reliability through complete processing logic while reducing package size by avoiding redundancy.
Solution Approach 2:
The patent creates a universal processing logic module that serves multiple effect editing types. Instead of having separate processing logic for each effect type, a single shared library contains processing logic that can be applied universally across different effects through parameter configuration. This multi-functionality reduces the overall package size while maintaining the completeness of processing logic.
2Reliability
If configuration files store complete processing logic for each effect editing type, then the processing is complete and reliable, but the development workload increases
Solution Approach 1:
The patent implements a universal processing logic module that can handle multiple effect editing types through parameter configuration rather than requiring separate logic for each effect. This universality significantly reduces development workload as developers only need to create and maintain one set of processing logic that serves all effect types, while still ensuring complete and reliable processing through proper parameter settings.
Solution Approach 2:
The patent uses parameter configuration files that reference shared processing logic, effectively copying the reference to the same logic rather than duplicating the logic itself. This allows the system to maintain complete processing logic for each effect type through parameter specifications while avoiding the repetitive manual creation of identical logic blocks, thereby reducing development workload.
3Adaptability or versatility
If configuration information stores all processing logic, then the effect editing is self-contained, but data redundancy increases
Solution Approach 1:
The patent segments configuration information into effect type identification and processing logic identification, separating the reference to processing logic from the logic itself. This segmentation allows effect editing to remain self-contained through parameter configuration while avoiding data redundancy by referencing shared processing logic stored in a common library rather than duplicating it in each configuration.
Solution Approach 2:
The patent introduces an intermediary layer in the form of a shared library that mediates between the configuration files and the actual processing logic. The configuration files contain references to this shared library rather than embedding the logic directly, which maintains the self-contained nature of effect editing through parameter specification while eliminating data redundancy through centralized logic storage.
Data Source
AI summary
Provided in the present disclosure are an image processing method and apparatus, and an electronic device and a storage medium. The method includes: obtaining configuration information matching effect editing in response to performing the effect editing on an initial image, wherein the configuration information includes a deep learning inference node for performing the effect editing on the initial image, and a pre-processing function node and a post-processing function node; calling processing logic of the pre-processing function node according to the configuration information to obtain input data; obtaining output data by means of an algorithm model corresponding to the deep learning inference node; and calling processing logic of the post-processing function node according to the configuration information to obtain a target image added with an effect.