Visual Effects Framework for High-Bit-Depth Image Processing
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Solution Overview
Problem
Existing visual effects tools are inefficient and difficult to scale, requiring manual selection and configuration for attributes like object size, shape, and background motion, and are limited to 8 bits per color channel, making them unsuitable for higher bit depth videos.
Innovation Solution
The method involves dividing video frames with higher color depth into partial images, applying compatible image processing techniques to each partial image, and merging the results, while automatically selecting techniques based on video attributes like background motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual selection and configuration of visual effects tools is used to account for object size, shape, and background motion, then visual effects can be applied with high precision, but processing time and operational complexity increase significantly
Solution Approach 1:
The system automatically analyzes video attributes (background motion, object characteristics) and selects appropriate image processing techniques without requiring manual configuration. The framework self-adjusts parameters and chooses processing methods based on real-time analysis of the video content, eliminating the need for visual effects artists to manually configure tools for each scene.
Solution Approach 2:
The visual effects processing system dynamically adapts its parameters and technique selection based on changing video attributes. As background motion and other attributes vary throughout the video, the system continuously adjusts its processing approach, selecting different techniques for different segments automatically, rather than using static manual configurations.
2Manufacturing precision
If deep-learning-based image processing techniques are used, then visual effects can be applied with high quality, but the techniques are limited to 8 bits per color channel and cannot process higher bit depth videos
Solution Approach 1:
The system segments the video processing task by separating videos with different bit depths (8-bit and higher bit depths) and applying appropriate processing techniques to each segment. The framework automatically identifies the bit depth of video segments and routes them to suitable processing pipelines, enabling high-quality processing for both 8-bit and higher bit depth content.
Solution Approach 2:
The system changes processing parameters based on the input video's bit depth. When detecting higher bit depth video, the framework adjusts its processing parameters and selects techniques compatible with the elevated color depth, rather than being restricted to fixed 8-bit processing parameters. This allows the same framework to maintain high visual effects quality across different bit depth formats.
3Ease of manufacture
If conventional visual effects tools are used, then processing can be applied to standard video formats, but the tools are inefficient and difficult to scale to support increased video content volume
Solution Approach 1:
The framework is designed as a universal system that can process multiple video formats, bit depths, and motion characteristics through a single automated pipeline. Rather than requiring separate manual configuration for each video type, the multi-functional framework automatically adapts to handle diverse video content, scaling efficiently from individual clips to large volumes of video material.
Data Source
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AI summary
One embodiment of the present invention sets forth a technique, which includes dividing an input image into a first partial image that stores a first subset of bits in each pixel of the input image and a second partial image that stores a second subset of bits that is disjoint from the first subset of bits in each pixel of the input image. The technique also includes modifying a first set of pixels in the first partial image to generate a first partial image processing result and modifying a second set of pixels in the second partial image to generate a second partial image processing result. The technique further includes generating a combined image processing result based on a combination of the first partial image processing result and the second partial image processing result.