Image Processing Device Block-Based Region of Interest Compression
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Solution Overview
Problem
Conventional image compression methods for surveillance camera systems increase data volume, leading to higher communication and storage costs, and often rely on post-processing techniques that require additional hardware functionality, making it difficult to effectively reduce data without impacting video quality.
Innovation Solution
An image processing device and method that preprocesses images by dividing them into blocks, determining regions of interest, and reducing information in non-interest blocks using a transformation unit, block-of-interest determination unit, and information amount reduction unit, independent of subsequent compression processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional image compression methods are used to reduce data volume, then communication and storage costs are reduced, but artifacts are generated at block boundaries and video quality deteriorates
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently processed to determine whether it contains a region of interest. This segmentation allows selective compression where needed, preventing artifacts in regions requiring high quality while reducing data volume in regions where compression is acceptable.
Solution Approach 2:
Different compression strategies are applied to different blocks based on whether they contain regions of interest. Blocks with regions of interest maintain higher quality with less compression, while blocks without regions of interest undergo more aggressive compression to reduce data volume. This local differentiation resolves the contradiction between overall data reduction and quality preservation.
2Quantity of substance
If low pass filtering is applied to reduce high frequency components, then data volume is reduced, but artifacts are suppressed only partially and compression efficiency is limited
Solution Approach 1:
The system performs preliminary processing by dividing the image into blocks and determining which blocks contain regions of interest before applying compression. This preliminary classification enables the compression algorithm to allocate resources more efficiently, focusing computational effort on blocks that require it, thereby improving overall compression efficiency while reducing data volume.
Solution Approach 2:
The compression parameters are dynamically adjusted based on the determined blocks. Blocks with regions of interest receive different compression parameters than blocks without regions of interest. This dynamic adaptation allows the system to optimize compression efficiency for each block type while achieving overall data volume reduction.
3Quantity of substance
If region-based compression is implemented, then data volume is reduced, but additional hardware functionality is required and device complexity increases
Solution Approach 1:
The block division and region determination functionality is integrated into the existing compression processing unit, allowing it to perform multiple functions: image blocking, region of interest detection, and adaptive compression parameter selection. This multi-functionality reduces the need for separate dedicated hardware components, thereby reducing device complexity while achieving region-based data reduction.
Data Source
AI summary
There is provided an image processing device including: a preprocessing unit configured to perform preprocessing for reducing an amount of information of an input image to generate a preprocessed image and supply the preprocessed image to an encoding processing unit that encodes and outputs the preprocessed image, wherein the preprocessing unit includes a transformation unit configured to divide the input image into predetermined blocks, perform transformation processing with a predetermined method, and generate transform blocks, a block-of-interest determination unit configured to hold region information indicating a region of interest or a non region of interest of the input image and determine whether or not each transform block is a block of interest on the basis of the region information, and an information amount reduction unit.