Adaptive Security Camera Image Compression via Selective Noise Reduction
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Solution Overview
Problem
Conventional security surveillance cameras face challenges in efficiently transmitting and storing high-resolution images over low-bandwidth networks, as they often result in data loss or theft, and existing compression methods do not effectively manage bandwidth and storage efficiently.
Innovation Solution
A lossy compression method that transforms sequential images into key frames and delta frames, using Huffman encoding and a configurable low pass filter to optimize bandwidth and storage by selectively reducing resolution and noise, allowing for adaptive compression based on image content and external triggers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional compression methods are used for security surveillance images, then bandwidth and storage are consumed efficiently, but image quality and detail are lost
Solution Approach 1:
The patent applies different compression quality levels to different regions of the image based on their importance. Critical regions (e.g., areas with motion, faces, or important objects) are maintained at higher quality with less compression, while less important background areas use higher compression. This is achieved through region-of-interest detection and adaptive quantization parameters that vary across different spatial locations in the image.
Solution Approach 2:
The system dynamically adjusts compression parameters such as quantization tables, DCT block sizes, and Huffman coding settings based on image content analysis. When important features are detected, the system changes parameters to preserve more detail; when compressing general areas, it uses more aggressive parameter settings to reduce bandwidth consumption.
2Manufacturing precision
If high-resolution images are transmitted over low-bandwidth networks, then image detail is preserved, but transmission efficiency and storage requirements decrease
Solution Approach 1:
The image is divided into multiple blocks or regions, each processed with different compression strategies. High-resolution transmission is applied only to segments containing important information, while other segments use lower resolution. This segmentation allows the system to preserve necessary detail while reducing overall bandwidth consumption.
Solution Approach 2:
Instead of applying uniform high-resolution transmission to the entire image, the system applies high resolution only partially to critical regions where it is necessary. This partial action approach maintains image detail where needed while significantly reducing the total bandwidth consumption compared to transmitting the entire image at high resolution.
3Reliability
If lossless compression is used, then image fidelity is maintained, but compression ratio and storage efficiency are reduced
Solution Approach 1:
The patent implements a hybrid approach where different regions of the image use different compression methods. Critical regions maintain lossless or near-lossless compression to preserve fidelity, while non-critical regions use lossy compression with higher ratios. This local differentiation allows the system to achieve both high fidelity where needed and high compression ratios overall.
Solution Approach 2:
The system dynamically changes compression parameters based on region importance. For lossless regions, parameters are set to preserve all data; for lossy regions, parameters are adjusted to maximize compression while maintaining acceptable quality. This adaptive parameter adjustment enables the system to achieve high overall compression ratios while maintaining fidelity in critical areas.
Data Source
AI summary
A lossy compression method optimizes bandwidth and storage for a security surveillance network. An appliance on a local network attached to event capture terminals transforms image files into a key frame and at least one subsequent frame. Decompression combines a subsequent frame with its key frame to provide an image with graduated resolution/noise clutter. A camera records, and forwards a plurality of image files compatible with JPEG encoding. Key frames are selected from among the plurality of image files. A configurable low pass filter is reset for each train of a key frame and its subsequent frame or frames. Each low pass filter is selectively applied to each pixel block within a subsequent frame. The transformation operates on coefficients of frequency bins. Meta data enables decompression of a single subsequent frame by reversing some of the transformations to provide a JPEG compatible file having selectively reduced resolution or noise clutter.