Image Processing System Wavelet Coefficient Scaling
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Solution Overview
Problem
Existing data compression methods for video and audio streaming are inefficient, leading to high bandwidth demands and increased costs, as they fail to provide optimal compression ratios, especially in congested wireless networks and with growing consumer expectations.
Innovation Solution
An image processing system that transforms input image blocks into wavelet coefficients, organizes them into bitplanes with adjusted priority values, and scales them for efficient coding and decoding, allowing for improved compression and reduced bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing data compression methods are used for video and audio streaming, then bandwidth demands increase and costs rise, but compression efficiency remains insufficient
Solution Approach 1:
The patent segments the image processing into distinct functional modules: wavelet transform module for decomposition, scaling correction module for priority adjustment, coding module for compression, and decoding module for reconstruction. This segmentation allows each module to optimize specific aspects of compression efficiency independently, resolving the contradiction between compression efficiency and bandwidth consumption.
Solution Approach 2:
The scaling correction module dynamically adjusts bitplane priority values as parameters, increasing priority for low pass subband and coarsest high pass subband. This parameter change enables adaptive compression that prioritizes important frequency components, improving compression efficiency while controlling bandwidth usage.
2Productivity
If wavelet coefficients are transformed and scaled with adjusted bitplane priorities, then compression efficiency improves, but processing complexity increases
Solution Approach 1:
The wavelet transform module performs multiple functions: decomposition of image blocks into frequency subbands, transformation of pixel data into wavelet coefficients, and preparation for scaling operations. This multi-functionality reduces overall system complexity by consolidating operations into a single universal module.
Solution Approach 2:
The scaling correction module performs preliminary action by pre-adjusting bitplane priority values before the actual coding process. This preliminary scaling of wavelet coefficients by powers of two simplifies subsequent compression operations, reducing processing complexity while maintaining high compression efficiency.
3Manufacturing precision
If bitplane priority values are increased for low pass and coarsest high pass subbands, then image quality improves, but coding complexity increases
Solution Approach 1:
The scaling correction module applies local quality by differentiating treatment among different subbands. Specifically, it increases bitplane priority values only for the low pass subband and coarsest high pass subband, while leaving other subbands unchanged. This localized adjustment preserves important image quality characteristics without unnecessarily complicating the coding process.
Data Source
AI summary
An image processing system, and a method of operation thereof, including: a capture device for obtaining an input image block; and an image signal processing hardware including: a wavelet transform module for generating a transformed image block based on the input image block, the transformed image block having wavelet coefficients, a coding module including a scaling correction module for organizing binary values of the wavelet coefficients into bitplanes and for generating a scaled wavelet block with a scaling factor, the coding module for generating a compressed image block by scanning and coding the bitplanes of the scaled wavelet block, and a decoding module and an inverse wavelet transform module for generating an output image block by decoding, rescaling, and performing an inverse wavelet transform on the compressed image block for display on a display device.