Spatial Image Subsampling for Display-Adaptive Video Encoding
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Solution Overview
Problem
Existing video encoding methods fail to efficiently reduce data quantity without compromising visual quality, especially when adapting to diverse display devices and user interactions.
Innovation Solution
A method involving spatial subsampling using multiple filters oriented in different directions, adjusted based on display device characteristics and user interactions, followed by encoding, and subsequent oversampling during decoding to maintain visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If spatial subsampling is applied to reduce data quantity, then data transmission requirements are reduced, but visual quality may be compromised
Solution Approach 1:
The patent applies different subsampling factors to different regions of the image based on their importance. Critical regions (such as faces or text) maintain higher resolution while less important regions are more heavily subsampled. This local differentiation allows significant data reduction while preserving visual quality in key areas.
Solution Approach 2:
The patent dynamically adjusts subsampling parameters based on image content analysis and display device characteristics. The subsampling factor is not fixed but adapts according to the importance of different regions, allowing the system to optimize between data reduction and quality preservation in real-time.
2Device complexity
If fixed subsampling is used, then encoding complexity is reduced, but adaptability to display devices and user interactions is lost
Solution Approach 1:
The system dynamically determines subsampling parameters based on detected display device characteristics and user interaction patterns. This allows the encoding process to adapt to different display configurations and user needs without requiring complex manual configuration, maintaining flexibility while managing computational complexity.
Solution Approach 2:
The system automatically analyzes image content and display characteristics to self-determine optimal subsampling parameters. This self-adjusting mechanism eliminates the need for complex external configuration while achieving high adaptability to different display devices and usage scenarios.
3Manufacturing precision
If high spatial resolution is maintained, then visual quality is preserved, but data transmission requirements increase
Solution Approach 1:
The patent segments the image into multiple regions with different resolution requirements. Instead of maintaining uniform high resolution across the entire image, it applies high spatial resolution only to critical segments (such as faces or important objects) while using lower resolution for background areas, significantly reducing overall data transmission requirements.
Solution Approach 2:
Different regions of the image are assigned different spatial resolution levels based on their visual importance. This local quality differentiation allows the system to preserve high spatial resolution where needed while reducing data transmission in less critical areas, achieving an optimal balance between quality and data efficiency.
Data Source
AI summary
Disclosed is a method for encoding images of a video sequence that includes implementing the following steps: c) obtaining an initial configuration representative of structural characteristics of a display device and, for an image of the video sequence referred to as the input sub-sequence, d1) carrying out a first spatial sub-sampling operation on the elements of the image by using a filter oriented in a first direction and including a first set of values of sub-sampling factors, the latter being determined as a function of the initial configuration, then inserting the resulting image into an output sub-sequence, and e) encoding the images of the output sub-sequence.