Adaptive Image Compression Using Luminance-Only Encoding
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Solution Overview
Problem
Existing image compression methods with high compression rates cause image distortion, which degrades the effectiveness of temporal noise reduction while increasing hardware costs, necessitating a balance between reducing distortion and saving hardware costs.
Innovation Solution
An adaptive image compression method using fixed-length codes (FLC) that selectively encodes either the luminance or both luminance and chrominance components of image data based on characteristic values, allowing for reduced image distortion and lower hardware costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high compression rate methods are used to save hardware costs, then memory requirements are reduced, but image distortion increases and temporal noise reduction effectiveness deteriorates
Solution Approach 1:
The patent applies different compression strategies to different regions of the image based on local characteristics. Motion-compensated prediction is applied selectively to regions with motion, while static regions use simpler compression. This allows achieving higher overall compression ratios while maintaining image quality in critical areas, thus reducing memory requirements without significantly increasing image distortion.
Solution Approach 2:
The image is divided into multiple blocks or regions, and different compression techniques are applied to each segment. Motion-compensated prediction operates on motion-active blocks while other blocks use standard compression. This segmentation approach enables the system to achieve better compression rates overall while preserving image quality where needed, resolving the contradiction between memory savings and image fidelity.
2Device complexity
If high compression rate methods are used, then hardware costs are reduced, but image distortion increases
Solution Approach 1:
The patent employs dynamic adjustment of compression parameters based on image content characteristics. The compression rate and technique are adaptively changed according to the complexity of the image region being compressed. This dynamic approach allows the system to achieve high compression rates for simple regions (reducing hardware/memory needs) while maintaining high quality for complex regions, thus reducing hardware costs without excessive image distortion.
3Quantity of substance
If compression is applied to reduce memory storage, then memory costs are saved, but temporal noise reduction effectiveness is degraded
Solution Approach 1:
The patent applies motion compensation and prediction techniques before the actual compression process. By predicting future frames based on previous frames and applying compensation for motion, the system prepares the data in a form that is more amenable to compression while preserving the temporal relationships needed for effective noise reduction. This preliminary action allows aggressive compression while maintaining the integrity of temporal information.
Solution Approach 2:
The system uses feedback from motion detection and image analysis to adjust compression parameters dynamically. When motion is detected, the system adjusts compression settings to preserve temporal information needed for noise reduction. When no motion is present, higher compression can be applied. This feedback mechanism ensures that temporal noise reduction effectiveness is maintained while achieving significant memory savings.
Data Source
AI summary
An image compression method and an image compressor are provided. The image compression method compresses the image data based on the fixed-length code (FLC) to generate the compressed data, and includes the following steps: determining whether a characteristic value of the image data meets a condition; encoding only the luminance component of the image data to generate the compressed data when the characteristic value meets the condition; encoding the luminance and chrominance components of the image data to generate the compressed data when the characteristic value does not meet the condition; and storing the compressed data.