Real-time Video Compression via EPG Content Settings
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Solution Overview
Problem
Current digital video broadcasting standards lack dynamic content-based compression, which can improve user experience and reduce transmission load, as they do not adapt compression techniques based on the content of the digital broadcast programs in real-time.
Innovation Solution
A system and method for real-time content-based compression of digital video broadcasts that involves obtaining compression settings from an electronic program guide (EPG), associating them with the video content, and using these settings to compress and distribute the broadcasts, allowing for dynamic adjustment of compression based on content type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional digital broadcasting standards are used, then video broadcasts can be transmitted, but compression cannot be dynamically adapted to content type in real-time
Solution Approach 1:
The system pre-processes video content to generate content descriptors and metadata before compression, enabling dynamic compression parameter selection. The content analysis module extracts features such as motion intensity, scene complexity, and content type classification in advance, which then guide the compression settings selection without adding real-time complexity to the compression process itself.
Solution Approach 2:
The system dynamically adjusts compression parameters based on real-time content analysis. Different compression settings are applied to different segments of video content based on their characteristics - for example, higher compression for static scenes and lower compression for high-motion scenes. This dynamic adaptation is enabled by the content-based compression module that continuously monitors and adjusts parameters.
2Loss of energy
If dynamic content-based compression is implemented, then transmission load is reduced, but system complexity increases
Solution Approach 1:
The compression system is divided into independent modular components: content analysis module, compression parameter selection module, and compression execution module. Each module performs a specific function and can be independently optimized or configured. This segmentation allows the system to reduce transmission load through intelligent compression while managing complexity by distributing functions across separate modules rather than requiring a monolithic complex system.
Solution Approach 2:
The patent introduces an intermediary content descriptor layer that bridges the gap between raw video content and compression parameters. Instead of directly analyzing complex video streams for compression decisions, the system uses simplified content descriptors and metadata as intermediaries. This intermediary representation reduces the computational complexity of the compression system while still enabling effective transmission load reduction through content-aware compression.
3Adaptability or versatility
If compression settings are manually configured, then system complexity is low, but adaptability to different content types is poor
Solution Approach 1:
The compression system performs self-service by automatically analyzing content characteristics and selecting appropriate compression parameters without external intervention. The content analysis module autonomously extracts features from video streams, and the parameter selection module automatically maps these features to optimal compression settings. This self-service capability enables high adaptability to different content types while maintaining reasonable automation levels that do not require complex external control systems.
Solution Approach 2:
The system implements feedback loops where compression performance metrics are continuously monitored and used to adjust compression parameters. The content analysis results feed into parameter selection, which then feeds into the compression process, and performance feedback loops back to refine future parameter selections. This feedback mechanism enables automatic adaptation to different content types while keeping the automation extent manageable through rule-based and heuristic approaches rather than requiring complex AI systems.
Data Source
AI summary
There are provided a method, a system and machine-readable medium for encoding a video broadcast. The method includes obtaining one or more first compression settings for the video broadcast from an electronic program guide (EPG), the EPG associating the video broadcast with the one or more first compression settings. The method further includes compressing the video broadcast using the one or more first compression settings into a distribution broadcast. Yet further, the method includes distributing the distribution broadcast. There is also provided a method, system and machine readable medium to provide compression settings for encoding a video broadcast. The method includes inserting one or more compression settings into an electronic program guide (EPG) in association with the video broadcast based on a content type of the video broadcast. The method further includes distributing the EPG.