Video Coding Noise Filtering with Gaussian Models
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Solution Overview
Problem
High definition video transmission over low bandwidth connections is challenging due to the contradiction between high resolution and low bandwidth, particularly in surveillance and wireless communication applications, where distinguishing real objects from noise in the background is difficult, leading to inefficient use of bandwidth.
Innovation Solution
A method and system that utilize Gaussian models to classify pixels as background or foreground, differentiate between semantically significant and insignificant motion, and update foreground objects at a higher rate than background, reducing unnecessary data transmission by marking repetitive background motion as noise and updating the background less frequently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high definition video is transmitted to maintain high resolution quality, then video quality is improved, but bandwidth consumption increases significantly
Solution Approach 1:
The video frame is divided into multiple blocks (e.g., 8x8 pixel blocks) that are processed independently. Each block can be updated at different frequencies based on its content characteristics, allowing selective transmission of only the most important information while reducing overall bandwidth consumption.
Solution Approach 2:
Different regions of the video frame are treated differently based on their content. Blocks containing significant motion or changes are updated at higher frequencies, while blocks with stable background content are updated at lower frequencies. This local differentiation optimizes the balance between video quality and bandwidth usage.
2Measurement precision
If all motion pixels are coded at high frame rate to capture all movement details, then motion accuracy is improved, but bandwidth consumption increases
Solution Approach 1:
Instead of coding all motion pixels at the maximum frame rate, the system applies partial updates based on the significance of motion in each block. Only blocks with significant motion changes are updated at high frame rates, while blocks with minor or repetitive motion are updated at lower rates, reducing overall bandwidth consumption while maintaining acceptable motion accuracy.
Solution Approach 2:
The frame rate parameter is dynamically adjusted for different video blocks based on their motion characteristics. Blocks with high motion variance use higher frame rates, while blocks with low motion variance use lower frame rates. This parameter adaptation optimizes the trade-off between motion accuracy and bandwidth consumption.
3Stability of the object's composition
If background pixels are updated frequently to maintain smooth video, then video smoothness is improved, but bandwidth consumption increases
Solution Approach 1:
Background blocks are updated at lower, periodic frame rates compared to foreground blocks. The system maintains video smoothness by periodically refreshing background information rather than continuously updating it, which reduces bandwidth consumption while preserving acceptable visual quality and smoothness.
Data Source
AI summary
A method of video coding includes receiving a video image having a plurality of pixels, selecting a plurality of Gaussian models for each pixel, classifying each pixel as a background or foreground pixel, determining if an object is always moving locally by detecting if an object is always moving within a predetermined distance and changing the direction of moving frequently for a second predetermined period of time, removing an object from classification as an object and ignoring motion of the object if the object is always moving locally, marking the area that the object moves within as a noisy area on a noise mask buffer, updating background models based on motion detection and noise filtering, updating current and evolving background images, and coding the current background image, background update blocks and objects for transmission.