Wavelet-Based Image Compression with Binary Decomposition
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Solution Overview
Problem
Current image processing systems face challenges in efficiently capturing, managing, and displaying digital images due to the need for larger storage capacities for high-quality images and videos, leading to increased costs and reduced performance in consumer and industrial electronics.
Innovation Solution
An image processing system that employs a wavelet-based embedded coder with low complexity, using binary decomposition of bitplanes and variable-length codes to achieve visually lossless compression, reducing bandwidth and memory demands by prioritizing lower frequency information and grouping zeroes for efficient coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wavelet transform and binary decomposition are applied to compress image data, then storage capacity and compression efficiency are improved, but device complexity increases
Solution Approach 1:
The patent applies wavelet transform to divide the image block into multiple frequency subbands (low-pass and high-pass components at different scales). This segmentation allows selective processing of frequency components, enabling efficient compression by handling different frequency information separately, which directly improves compression efficiency while maintaining manageable complexity through structured decomposition
Solution Approach 2:
The patent transforms image data from the spatial domain to the wavelet domain by changing the representation parameters. The wavelet coefficients provide an alternative parameterization that concentrates energy in fewer coefficients, enabling lossless compression at lower bit rates. This parameter transformation is the core mechanism that achieves improved compression efficiency
2Quantity of substance
If higher compression ratios are achieved through wavelet-based coding, then bandwidth requirements are reduced, but processing time increases
Solution Approach 1:
The patent performs wavelet transform and binary decomposition as preliminary processing steps before actual compression coding. By pre-organizing the image data into wavelet subbands and identifying significant coefficients in advance, the system prepares the data structure that enables efficient subsequent coding operations, reducing overall processing time for high-ratio compression
Solution Approach 2:
The patent extracts and identifies significant wavelet coefficients that carry essential image information, separating them from insignificant coefficients that can be discarded or processed later. This extraction approach allows the system to focus computational resources only on the most important data elements, reducing processing time while achieving high compression ratios
Data Source
AI summary
An image processing system, and a method of operation thereof, includes: a pre-processing module for receiving a raw image block of a source image from an imaging device; a wavelet transform module, coupled to the pre-processing module, for forming a wavelet coefficient block by performing a wavelet transform operation on the raw image block; and an encoding module, coupled to the wavelet transform module, for initializing a region significance vector based on the wavelet coefficient block, for generating a code value based on the region significance vector at an index position of a bit region in a wavelet bitplane of the wavelet coefficient block, for forming an encoded block based on the code value, and for generating a bitstream based on the encoded block for decoding into a display image to display on a display device.