Wavelet Subband Decorrelation for Raw Image Compression
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Solution Overview
Problem
Current image and video processing systems face challenges in efficiently compressing raw data from digital cameras, particularly due to the color filter array, which requires effective lossless or low-loss compression schemes to manage high frame rates and real-time processing demands.
Innovation Solution
The method involves a one-level wavelet transform to generate LL, HH, LH, and HL subbands, followed by decorrelation and additional n-level wavelet transformations to produce sparsified subbands for encoding, leveraging the correlation between LH and HL subbands to enhance compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wavelet transform is applied to compress raw sensor data, then compression efficiency is improved, but the complexity of processing increases due to the color filter array structure
Solution Approach 1:
The patent segments the raw sensor data processing by separating the luminance and chrominance components through decorrelation of wavelet subbands. The LH and HL subbands are processed differently from LL and HH subbands, allowing specialized compression strategies for each segment that improve overall efficiency while managing complexity through modular processing stages.
Solution Approach 2:
The patent applies a composite approach by combining multiple transformation techniques: initial wavelet transform followed by decorrelation operations, then additional wavelet transforms on specific subbands. This composite processing pipeline leverages the strengths of each technique to achieve superior compression while distributing computational complexity across multiple specialized stages rather than one complex operation.
2Measurement precision
If lossless compression is used to preserve image quality, then reconstruction accuracy is improved, but compression ratio deteriorates compared to lossy compression
Solution Approach 1:
The patent changes the statistical parameters of the data by applying wavelet transforms and decorrelation operations that redistribute energy across different frequency subbands. This parameter transformation creates sparser representations that are more amenable to compression, allowing lossless schemes to achieve better ratios by exploiting the transformed data structure rather than compressing raw pixel values directly.
3Productivity
If high frame rates are processed in real-time, then productivity is improved, but processing speed requirements increase making compression more difficult
Solution Approach 1:
The patent performs preliminary wavelet transforms and decorrelation operations on the raw sensor data immediately after capture, before the main compression stage. This preliminary processing prepares the data in a more compressible format, reducing the computational burden on subsequent real-time compression operations and enabling higher frame rates to be processed efficiently.
Data Source
AI summary
A method for processing image or video data is performed in an image processing pipeline. Color filtered mosaiced raw image or video data is received. A one-level wavelet transform of subbands of the color filtered mosaiced raw image or video data to provide LL, HH, LH and HL subbands. The LH and HL subbands are de-correlated by summing and difference operations to provide decorrelated sum and difference subbands. Additional n-level wavelet transformation on the sum and difference subbands and the LL and HH subbands to provide sparsified subbands for encoding. LL and HH and sum subbands are recombined into standard color images e.g., red, green, and blue color components, which are subsequently processed by color correction, white balance, and gamma correction. The sparsified subbands are encoded.