Video Compression Using Fuzzy Motion Vectors to Preserve Image Detail
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Solution Overview
Problem
Existing video data processing methods, such as H.264, result in data loss during compression, leading to poor display quality due to the loss of image details in compressed video data.
Innovation Solution
A method involving the acquisition of motion vectors using fuzzy algorithms to determine object outlines in adjacent frames, followed by intra-frame compression and data grouping based on luminance and chroma differences, reduces data loss and improves display quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If compression technology is used to reduce data bandwidth, then storage and processing capability is improved, but image detail loss occurs leading to poor display quality
Solution Approach 1:
The patent segments the video data into macroblocks and further divides them into luminance components and chroma components. Different compression rates are applied to different components based on their importance, with luminance receiving higher compression resistance than chroma, thereby preserving critical image details while reducing overall data volume.
Solution Approach 2:
The patent applies different compression strategies to different regions and components within the video data. Motion vectors are calculated for macroblocks, and compression parameters are adjusted locally based on motion detection results, allowing optimal compression that preserves details in important regions while aggressive compression in less critical areas.
2Loss of information
If motion vectors are accurately determined using fuzzy algorithms, then image detail retention is improved, but processing complexity increases
Solution Approach 1:
The patent replaces traditional mechanical edge detection methods with fuzzy logic algorithms to determine motion vectors. The fuzzy algorithm processes outline information from adjacent frames to calculate motion vectors, providing more accurate motion detection that better preserves image details during compression while managing computational complexity through efficient fuzzy set operations.
3Productivity
If data compression is applied to reduce transmission size, then transmission efficiency is improved, but display quality deteriorates due to data loss
Solution Approach 1:
The patent applies partial compression to different components of the video data. Luminance components receive less aggressive compression compared to chroma components, as luminance carries more critical visual information. This selective compression approach maintains display quality for important elements while achieving sufficient transmission efficiency.
Solution Approach 2:
The patent uses motion vector information as feedback to adjust compression parameters. By calculating motion vectors from outline comparisons between frames, the system dynamically adapts compression strength based on actual motion content, preserving details in regions with significant motion while allowing higher compression in static or slowly moving regions.
Data Source
AI summary
Provided is a method for processing video data. The method includes: acquiring a second outline of at least one object in an (n+1)th image frame of a to-be-compressed video data group based on a fuzzy algorithm; determining a motion vector of the at least one object according to the second outline and a first outline of the at least one object in an nth image frame of the to-be-compressed video data group; acquiring compressed video data according to the motion vector and a start image frame of to-be-compressed video data; and sending the compressed video data to an apparatus for displaying images.