Compressive sensing video encoding with non-uniform sampling
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Solution Overview
Problem
Existing camera systems, particularly wearable cameras, face high energy consumption due to traditional image sensors like CMOS and CCD, which can be mitigated by compressive sensing techniques but require improvements in video encoding and decoding to maintain image quality while reducing energy usage.
Innovation Solution
A video coding apparatus and method that utilize a processor to obtain a temporal and non-uniform compressive sensing sampling matrix for encoding and a sparsifying transform based on discrete cosine and wavelet transforms for decoding, enhancing image quality and reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional image sensors (CMOS, CCD) are used for direct measurement, then image quality is maintained, but energy consumption increases
Solution Approach 1:
The patent extracts only the essential information from the image scene by using compressive sensing to capture a reduced set of measurements. Instead of reading out every pixel, the system selectively samples a subset of pixels according to a sampling matrix, extracting the minimum necessary data to represent the image while discarding redundant information that can be recovered through computational methods
Solution Approach 2:
The patent changes the sampling parameters by using temporal and non-uniform varied sampling matrices that adapt to different video content characteristics. The sampling rate and pattern are dynamically adjusted based on scene complexity, motion detection, and temporal redundancy analysis, allowing the system to maintain image quality for complex scenes while reducing sampling for simpler frames
2Use of energy by moving object
If compressive sensing with uniform sampling matrix is used, then energy consumption is reduced, but image quality and artifact reduction deteriorate
Solution Approach 1:
The patent implements dynamic sampling matrices that change over time and adapt to the specific characteristics of each video frame. The sampling pattern is adjusted based on temporal redundancy analysis, motion detection results, and scene complexity, transforming the static uniform sampling approach into a dynamic adaptive sampling system that optimizes both energy efficiency and image quality
Solution Approach 2:
The patent performs preliminary analysis of video content characteristics before capturing frames, using motion detection and temporal redundancy assessment to determine optimal sampling strategies in advance. This preliminary action allows the system to pre-determine which frames require full sampling and which can use reduced sampling, optimizing energy consumption before the actual capture process
3Productivity
If discrete cosine transform is used for sparsifying, then encoding efficiency is improved, but decoding artifacts increase
Solution Approach 1:
The patent combines multiple transform methods (discrete cosine transform, discrete wavelet transform, and other sparsifying transforms) into a composite transformation pipeline. Different transforms are applied to different frequency components or spatial regions of the image, leveraging the strengths of each transform type to achieve better overall sparsity while minimizing the artifacts that would result from using a single transform alone
4Object-generated harmful factors
If discrete wavelet transform is used for sparsifying, then artifact reduction is improved, but encoding complexity increases
Solution Approach 1:
The patent segments the image or video data into different spatial regions, frequency bands, or temporal segments, and applies different sparsifying transforms to each segment. This segmentation allows the system to use computationally intensive transforms like wavelet transforms only where they provide the most benefit, while using simpler transforms in regions where they are sufficient, thereby reducing overall encoding complexity while maintaining artifact reduction performance
Data Source
AI summary
A video coding apparatus for encoding a compressive sensing signal has a processor. The processorobtains a compressive sensing sampling matrix; andcaptures the compressive sensing signal representing image data based on the compressive sensing sampling matrix, wherein the compressive sensing sampling matrix is non-uniform varied.