Image Transcoding Lookup Tables for Low Complexity Processing
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Solution Overview
Problem
Current image transcoding methods fail to optimize image quality while adhering to terminal constraints such as resolution and file size, particularly in multimedia messaging services, as they do not effectively combine quality factors and scaling factors to maximize user experience.
Innovation Solution
A method and system that generate transcoding parameters by optimizing quality and scaling factors for input images based on terminal characteristics, using a dataset of training images to select the best transcoding quality factor and scaling factor, which ensures near-optimal image quality and compliance with terminal constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optimal quality transcoding is performed by exhaustively searching all combinations of quality factor and scaling factor, then image quality is maximized, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent pre-computes and stores optimal transcoding parameters (quality factor and scaling factor combinations) in lookup tables during an offline training phase. During online transcoding, the system simply queries these pre-stored tables based on terminal characteristics and input image features, avoiding exhaustive search and significantly reducing computational complexity while maintaining near-optimal image quality.
Solution Approach 2:
The patent creates simplified models (lookup tables) that copy the essential relationships between transcoding parameters and image quality outcomes. These models are trained offline using comprehensive data, then deployed for rapid online decision-making, replacing complex real-time optimization with efficient table-based queries.
2Manufacturing precision
If comprehensive transcoding parameter optimization is performed, then image quality is maximized, but processing speed decreases
Solution Approach 1:
The system performs the computationally intensive parameter optimization work in advance during offline training, storing results in lookup tables. During online operation, transcoding decisions are made by simple table queries, dramatically increasing processing speed while maintaining high image quality through the pre-optimized parameters.
Solution Approach 2:
The patent separates the optimization process into periodic offline training phases (where comprehensive optimization occurs) and continuous online querying phases (where fast lookup operations occur). This periodic execution of heavy computation followed by rapid application maintains both high image quality and fast processing speed.
3Device complexity
If simple transcoding methods are used, then processing complexity is reduced, but image quality deteriorates
Solution Approach 1:
The patent embeds the intelligence of complex optimization into pre-computed lookup tables created during offline training. The online transcoder uses simple table-based queries that appear computationally simple but actually encode sophisticated quality optimization, achieving both low processing complexity and high image quality.
Solution Approach 2:
The lookup tables serve as intermediaries between the complex optimization process and the simple online transcoder. They translate comprehensive quality considerations into actionable transcoding parameters that can be quickly retrieved and applied, bridging the gap between complex optimization and simple execution.
4Manufacturing precision
If exhaustive parameter search is performed for each transcoding operation, then optimal quality is achieved, but processing time increases
Solution Approach 1:
The patent performs exhaustive parameter searches in advance during offline training and stores the results in lookup tables. During online transcoding, the system retrieves pre-determined optimal parameters from these tables based on terminal characteristics and input image features, reducing processing time from exhaustive search to simple table lookup while maintaining optimal quality.
Solution Approach 2:
The patent creates compact copies of optimal parameter relationships in lookup tables during offline training. These copied relationships enable rapid online decision-making without repeating the time-consuming exhaustive search, preserving optimal quality while dramatically reducing processing time.
Data Source
AI summary
Method and system for low complexity transcoding of images with near optimal quality for display on a terminal are presented. Generating effective transcoding parameters prior to transcoding and retrieving the transcoding parameters based on the features of the input image and the characteristics of the terminal, an output image quality close to that produced by optimal quality transcoding is achieved. The processing time is much smaller in comparison to that required for optimal quality transcoding.