Video Compression Using Frame Difference Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video compression techniques face challenges in handling high-bandwidth video data, particularly with complex scenes in motion, leading to computational burdens and loss in image clarity during real-time transmission.
Innovation Solution
A method for compressing video data by calculating differences between frames, identifying clusters of image data, processing outer rim nodes, eliminating redundant nodes, and recursively filling internal points, while placing and compressing frame colors in blocks to reduce bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video data is compressed using traditional CPU-based methods, then bandwidth requirements are reduced, but image clarity is lost due to computational limitations
Solution Approach 1:
The patent segments video frames into multiple blocks and processes each block independently through parallel operations. Difference calculation, clustering, and compression are performed on segmented regions simultaneously, enabling high-speed processing without sacrificing image quality details.
Solution Approach 2:
The patent performs preliminary difference calculation between current and previous frames to identify changed regions before compression. By pre-identifying difference clusters and their boundaries, the system prepares data structures that facilitate efficient compression while preserving important visual information.
2Productivity
If real-time video transmission is enabled through large data compression, then transmission speed is improved, but picture quality is compromised
Solution Approach 1:
The patent extracts only the essential difference information from video frames by identifying difference clusters and their boundary nodes. By taking out and transmitting only the critical change data rather than compressed full-frame data, the system achieves both fast transmission and preserved picture quality.
Solution Approach 2:
The patent transforms spatial video data into a different representation dimension by organizing difference information into cluster structures with boundary nodes and internal points. This dimensional transformation enables efficient encoding that maintains visual fidelity while reducing data volume for rapid transmission.
3Productivity
If difference calculation is performed between successive video frames, then compression efficiency is improved, but computational time increases
Solution Approach 1:
The patent divides the difference calculation process into segmented operations performed on video blocks rather than entire frames. By calculating differences on segmented regions and processing them through parallel clustering operations, the system achieves high compression efficiency with reduced computational time.
Solution Approach 2:
The difference calculation process automatically identifies difference clusters and their boundaries without requiring external intervention or complex algorithms. The system uses self-organizing cluster formation where difference points naturally group together, enabling efficient compression with minimal computational overhead.
Data Source
AI summary
A video compression system and method to isolate and capture video data for video compression, thereby eliminating the need for motion vectors and blocks. When data is captured from a video input device, such as a video camera, the data is captured and isolated into clusters of difference image data. The clusters are then scanned, filled, marked, split and blended in in order to compress the video data.