Video Frame Metadata Packaging for AI Exposure and Color Conversion
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Solution Overview
Problem
Current digital image processing technologies lack efficient methods for improving image exposure and converting monochromatic images to color, especially in the context of movie production, where traditional methods are time-consuming and less accurate.
Innovation Solution
The integration of artificial intelligence technology, employing principles of sensitometry to enhance image exposure and automatic colorization, using frame metadata and blockchain-based transaction processing to ensure authenticity and provenance, with systems like frame slicing and packaging machines that include units for object detection, recognition, and metadata assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional digital image processing methods are used for improving image exposure and color conversion, then the processing can be completed with existing technology, but the processing time is lengthy and the accuracy is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods with artificial intelligence algorithms. Specifically, AI technology is used to improve image exposure by learning from training data and automatically adjusting exposure parameters, and to convert monochromatic images to color through neural network-based colorization algorithms. This substitution of AI for traditional processing methods simultaneously improves accuracy and reduces processing time.
2Productivity
If artificial intelligence technology is applied to improve image exposure and convert monochromatic images to color, then processing accuracy and efficiency are significantly improved, but the complexity of the processing system increases
Solution Approach 1:
The patent implements self-service through automated AI processing pipelines. The system automatically performs image exposure improvement and color conversion without requiring manual intervention for each image. The AI algorithms self-adjust parameters based on learned patterns from training data, and the system automatically manages the workflow from input images through processing to output, reducing the need for complex manual control mechanisms.
3Reliability
If cryptographic hashing and watermarking are implemented to ensure authenticity and provenance, then the reliability of digital image products is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by implementing cryptographic hashing and watermarking at the point of image creation and processing. Rather than adding these security measures later in the workflow, the system embeds authentication data and provenance information during the initial AI processing stages. This preliminary implementation ensures authenticity is built-in from the start, reducing the need for separate verification steps later.
Data Source
AI summary
There are disclosed methods and apparatus for enhancing the image exposure of image inventories. A frame conversion and packaging machine assigns metadata to each frame of a digital video work. It takes objects that have been detected, recognized and assigned metadata to the objects by the frame slicing and packaging machine and enhances the image exposure and assigns new items of metadata to the enhanced-exposure modified images. The frame conversion and packaging machine then generates a cryptographic hash of the enhanced-exposure modified image's digital file. The frame conversion and packaging machine writes the hash to a node of a transaction processing network. Further, there are disclosed methods and apparatus for conversion of monochromatic image inventories to colored image inventories. A frame color-conversion and packaging machine assigns metadata to each frame of a digital video work. It takes objects that have been detected, recognized and assigned metadata to the objects by the frame slicing and packaging machine and converts the original monochromatic images to fully colorized images and assigns new items of metadata to the color-converted images. The frame color-conversion and packaging machine then generates a cryptographic hash of the color-converted modified image's digital file. The frame color-conversion and packaging machine writes the hash to a node of a transaction processing network.