Scalable Image Compression Algorithm Resolution Management
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Solution Overview
Problem
Digital cameras face challenges in efficiently compressing and reconstructing image data, as existing algorithms either compromise on quality or are slow, and the large data generated requires efficient compression methods to manage bandwidth and display limitations.
Innovation Solution
A compression algorithm that operates on a color-by-color basis, allowing for flexible resolution adjustment and using techniques like wavelet transforms and look-up tables to optimize image data processing, enabling efficient compression and decompression while maintaining visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a fast reconstruction algorithm is used, then processing speed is improved, but image quality deteriorates
Solution Approach 1:
The system dynamically selects reconstruction algorithms based on operational mode (real-time vs. post-processing) and adjusts processing parameters to balance speed and quality requirements. The camera can switch between fast approximate reconstruction for preview and higher quality reconstruction for final output.
Solution Approach 2:
The patent changes algorithmic parameters such as transform depth, quantization precision, and processing resolution to optimize the trade-off between reconstruction speed and image quality. Different parameter sets are applied based on whether real-time performance or maximum quality is the priority.
2Manufacturing precision
If a slow reconstruction algorithm is used, then image quality is improved, but processing speed deteriorates
Solution Approach 1:
The reconstruction process is segmented into multiple stages: a fast initial reconstruction for real-time preview, followed by optional progressive enhancement passes that improve quality for saved or displayed images. This allows the system to provide both speed and quality at different times in the workflow.
3Manufacturing precision
If full-resolution image reconstruction is performed, then image detail is improved, but data access requirements increase
Solution Approach 1:
The system performs partial reconstruction at full resolution only for regions or channels that require it, while using lower resolution or fewer color channels for other portions of the image. This selective approach reduces the total data access volume while maintaining necessary image detail where required.
4Quantity of substance
If compression is applied to reduce data size, then bandwidth efficiency is improved, but image resolution may be limited
Solution Approach 1:
The compression system applies different compression ratios and quality levels to different regions of the image or different color channels, preserving high resolution in important areas while allowing greater compression in less critical areas. This maintains overall image quality while achieving efficient data reduction.
Data Source
AI summary
The disclosure herein relates to devices for compression, decompression or reconstruction of image data for still or moving pictures, such as image data detected with a digital camera. In some embodiments, data channels are compressed using a scalable compression algorithm. The compression algorithm may allow customization of compression parameters, such as a quantization factor, code block size, number of transform levels, reversible or irreversible compression, a desired compression ratio with a variable bit rate output, a desired fixed bit rate output with a variable compression rate, progression order, output format, or visual weighting. A lower quality image or an image with lower resolution may be reconstructed using only some of the compressed data. Use of offsets to various layers and color channels allow reconstruction of the image without requiring decompression of all of the full image data.