Integral Image Compression for Random Access Storage
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Solution Overview
Problem
Modern imaging devices face challenges in efficiently capturing, managing, and displaying digital images due to increasing data sizes, which require larger storage and bandwidth, limiting their utility and necessitating improved image processing systems.
Innovation Solution
An image processing system that receives a random access block of an integral image, calculates a chosen mode and quantization number, forms a quantized block, calculates a predictor block, and generates a fixed length coding residual to create an image bitstream, enabling efficient compression and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image format sizes and recording speeds are increased to capture more digital image information, then the amount of information that can be captured improves, but the storage space and bandwidth requirements increase
Solution Approach 1:
The patent extracts only the essential visual information from the image data by identifying and removing redundant components. The visual importance map extracts salient features, and the conditional encoding extracts only necessary data elements, discarding redundant information to reduce storage requirements while preserving critical visual content.
Solution Approach 2:
The patent changes the parameter representation of image data by converting spatial information into a visual importance metric space. The visual importance map transforms image parameters into importance values, and the conditional encoding adjusts encoding parameters based on these importance values, enabling efficient compression without losing critical visual information.
2Quantity of substance
If image format sizes and recording speeds are increased to capture more digital image information, then the amount of information that can be captured improves, but the bandwidth required for transmission increases
Solution Approach 1:
The patent extracts only the essential visual information from the image data by identifying and removing redundant components. The visual importance map extracts salient features, and the conditional encoding extracts only necessary data elements, discarding redundant information to reduce storage requirements while preserving critical visual content.
Solution Approach 2:
The patent changes the parameter representation of image data by converting spatial information into a visual importance metric space. The visual importance map transforms image parameters into importance values, and the conditional encoding adjusts encoding parameters based on these importance values, enabling efficient compression without losing critical visual information.
3Volume of stationary object
If traditional image compression methods are used to reduce data size, then storage efficiency improves, but random access capability and image quality are compromised
Solution Approach 1:
The patent segments the image into regions based on visual importance, creating a hierarchical structure where different regions are encoded with different precision. This segmentation allows selective compression of less important regions while preserving critical visual information in important regions, maintaining both compression efficiency and image quality.
Solution Approach 2:
The patent applies different encoding qualities to different regions of the image based on their visual importance. High-importance regions receive higher encoding precision while low-importance regions use more aggressive compression, enabling differentiated quality preservation that maintains overall image quality while improving storage efficiency.
4Volume of stationary object
If traditional image compression methods are used to reduce data size, then storage efficiency improves, but transmission speed is reduced
Solution Approach 1:
The patent segments the image into regions based on visual importance, creating a hierarchical structure where different regions are encoded with different precision. This segmentation allows selective compression of less important regions while preserving critical visual information in important regions, maintaining both compression efficiency and image quality.
Solution Approach 2:
The patent applies different encoding qualities to different regions of the image based on their visual importance. High-importance regions receive higher encoding precision while low-importance regions use more aggressive compression, enabling differentiated quality preservation that maintains overall image quality while improving storage efficiency.
Data Source
AI summary
A system and method of operation of an image processing system includes: an imaging device for receiving a source image; a mode module for receiving a random access block of an integral image formed by summing a portion of the source image, and for calculating a chosen mode and a quantization number from the random access block; a quantization module for forming a quantized block by shifting the random access block based on the chosen mode; a predictor module for calculating a predictor block based on the quantized block; and a fixed length coding module for calculating a residual block by subtracting the predictor block from the quantized block, for calculating a fixed length coding residual based on the chosen mode and the residual block, and for forming an image bitstream having the quantization number and the fixed length coding residual.