Image Encoding Mode Selection via Cost-Based Reconfigurable Circuit
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Solution Overview
Problem
Machine-learned mode determination engines in image encoding may produce unintended results, leading to inefficiencies and potential extreme deterioration in image quality, especially when constraints on encoded data transmission are present.
Innovation Solution
An image encoding method and device that utilize both conventional and machine-learning based mode determination engines, where a reconfigurable circuit determines a second mode and selects between modes based on cost calculations to minimize risks, with the conventional engine acting as a fail-safe when the machine-learning mode's cost is higher than a predetermined value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine-learned mode determination engine is used, then encoding efficiency may be improved, but the risk of producing inappropriate results increases
Solution Approach 1:
The patent implements a cost calculation mechanism that evaluates the appropriateness of modes determined by the machine-learned engine before final encoding. By calculating a cost value based on encoding parameters and comparing it against threshold values, the system prepares in advance to correct potentially inappropriate decisions, thus cushioning against the risks of machine learning errors while maintaining its efficiency benefits.
Solution Approach 2:
The system establishes a feedback loop where the cost calculator evaluates the output of the machine-learned mode determination engine. The cost value is computed based on encoding parameters and fed back to assess whether the selected mode is appropriate. This feedback mechanism allows the system to verify and correct machine learning decisions, balancing improved encoding efficiency with reduced risk of inappropriate results.
2Productivity
If machine learning is used for mode determination, then more efficient encoding may be achieved, but image quality may deteriorate when transmission quantity is restricted
Solution Approach 1:
The patent calculates encoding costs in advance based on the mode selected by the machine-learned engine and compares these costs against threshold values. This beforehand evaluation acts as a cushion to prevent selection of modes that would lead to poor image quality under transmission constraints, while still allowing the machine learning approach to drive overall encoding efficiency.
Solution Approach 2:
The cost calculation and threshold comparison mechanism provides feedback on the quality implications of machine-learned mode selections. By evaluating encoding parameters and costs before final encoding, the system can adjust or reject modes that would compromise image quality, thus maintaining manufacturing precision while benefiting from machine learning efficiency improvements.
3Reliability
If only conventional theoretical algorithms are used for mode determination, then reliability is maintained, but encoding efficiency is limited
Solution Approach 1:
The patent merges the machine-learned mode determination engine with conventional cost calculation methods. The machine learning component provides efficient mode suggestions while the conventional cost evaluation ensures reliability through systematic assessment of encoding parameters. This combination allows the system to achieve both improved encoding efficiency from machine learning and maintained reliability from conventional algorithms.
Solution Approach 2:
The cost calculation mechanism serves as an intermediary between the machine-learned engine and the final encoding process. It translates the machine learning output into evaluable cost metrics that reflect reliability considerations, bridging the gap between efficient machine learning decisions and reliable conventional encoding practices.
Data Source
AI summary
An image encoding method includes, using an image as input, determining a first mode suited to encode the image in accordance with a first processing procedure; using the image as input, determining a second mode suited to encode the image in accordance with a second processing procedure; selecting one of first mode and the second mode as a final mode; encoding the image, using the final mode; and calculating a cost of using the second mode to encode the image. The second processing procedure is implemented by a reconfigurable circuit. In the selecting, the first mode is selected when the cost calculated in the calculating is higher than a first predetermined value, and the second mode is selected when the cost is lower than or equal to the first predetermined value.