Binary Adaptive Golomb Coding for Image Data Compression
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Solution Overview
Problem
Modern imaging devices face challenges in capturing and displaying digital images efficiently due to the need for larger storage capacities for increased image formats and recording speeds, leading to a requirement for improved image processing systems that reduce data while maintaining quality.
Innovation Solution
An image processing system that employs a method involving wavelet or discrete cosine transforms, significance vector calculations, run-length coding, and Golomb coding to compress image data, forming an encoded bitstream that reduces data volume without introducing visible artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image format size and recording speed are increased, then image quality and capture capability are improved, but storage capacity requirements increase
Solution Approach 1:
The image data is divided into multiple blocks that are processed and encoded independently. Each block undergoes transform operations and coding processes separately, allowing for efficient compression while maintaining overall image quality. This segmentation enables the system to handle large images without requiring proportional increases in storage capacity.
Solution Approach 2:
The system dynamically adjusts encoding parameters including Golomb coding order, run-length coding parameters, and transform coefficients based on the statistical properties of each image block. This adaptive parameter adjustment optimizes the compression ratio for each block, maximizing storage efficiency while preserving image quality through intelligent parameter selection rather than uniform compression.
2Quantity of substance
If data compression is applied to reduce storage needs, then storage capacity is improved, but image quality may deteriorate
Solution Approach 1:
Different regions of the image are processed with different coding strategies based on their local characteristics. Blocks with higher frequency content or more detail are encoded with higher precision, while smoother regions use more aggressive compression. This local quality adjustment ensures that compression does not uniformly degrade image quality but rather optimizes it region-by-region.
Solution Approach 2:
The encoding process incorporates feedback mechanisms where the decoded blocks are compared with original blocks to calculate error metrics. This feedback is used to adjust subsequent encoding parameters, ensuring that compression maintains acceptable image quality. The feedback loop allows the system to adapt compression strength based on actual quality degradation observed in previous blocks.
3Productivity
If complex encoding processes are used to compress image data, then compression efficiency is improved, but processing time and computational complexity increase
Solution Approach 1:
The complex encoding process is segmented into multiple independent stages: transform operations, significance determination, coefficient coding, and block assembly. Each stage processes only a portion of the data independently, allowing for parallel processing and reducing the computational burden on any single operation. This segmentation maintains high compression efficiency while distributing computational complexity across multiple manageable tasks.
Solution Approach 2:
The encoding system dynamically adjusts the number and type of transform coefficients applied to each block based on its content. Rather than uniformly applying complex transforms to all blocks, the system adapts the transformation depth and complexity to match the actual detail content, maximizing compression efficiency for complex regions while minimizing processing for simple regions.
Data Source
AI summary
A system and method of operation of an image processing system includes: a pre-processing module for receiving a raw image block of a source image from an imaging device; a transform module for forming a coefficient block by performing a transform operation on the raw image block, and the transform operation including a wavelet transform or a discrete cosine transform; and an encoding module wherein the encoding module is for calculating a coefficient significance vector, a partition significance vector, a run-length coded value, and a Golomb coded value for a portion of a significant partition, and forming an encoded block in an image bitstream having the run-length coded value and the Golomb coded value, and the encoded block.