Adaptive Sub-Block Image Compression for Storage and Power Reduction
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Solution Overview
Problem
Current image data compression techniques face challenges in reducing storage requirements and power consumption, particularly when dealing with texture data and transmitting data from image processing systems, as they often require significant storage space and power to handle varying image characteristics across different channels.
Innovation Solution
The method involves dividing image data into sub-blocks based on their unique characteristics, allowing for the optimal selection of compression techniques such as lossless, lossy, or prediction-based compression for each sub-block, and generating an indirection table for efficient access and storage, thereby adapting to the content-specific needs of each channel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single compression technique is used for all sub-blocks, then device complexity is reduced, but compression efficiency and storage optimization deteriorate
Solution Approach 1:
The image data is divided into multiple sub-blocks, and different compression techniques are applied to different sub-blocks based on their characteristics. This segmentation allows the system to optimize compression efficiency for each region while maintaining manageable complexity through modular processing.
Solution Approach 2:
Different compression techniques are selected for different sub-blocks based on local data characteristics such as color channel content, texture complexity, and frequency distribution. This local optimization ensures that each region is compressed with the most appropriate method, improving overall compression efficiency without requiring a completely complex global system.
2Reliability
If lossless compression is applied to all sub-blocks, then image quality is preserved, but storage requirements and processing time increase
Solution Approach 1:
The system applies different compression strategies to different sub-blocks based on local image characteristics. Regions requiring high fidelity (such as those with important visual information) receive lossless compression, while regions tolerant of compression artifacts receive lossy compression, optimizing the balance between image quality and storage requirements.
Solution Approach 2:
The compression technique parameters are dynamically adjusted based on sub-block characteristics such as color channel content, frequency distribution, and visual importance. This allows the system to transition between lossless and lossy compression modes optimally for each region, reducing overall storage requirements while maintaining acceptable image quality.
3Quantity of substance
If different compression techniques are applied to each sub-block, then storage requirements are reduced, but device complexity and processing overhead increase
Solution Approach 1:
The image is segmented into sub-blocks that can be processed independently with different compression techniques. This segmentation reduces storage requirements by allowing optimized compression for each region while managing complexity through divide-and-conquer processing strategies.
Solution Approach 2:
Compression parameters and techniques are dynamically changed based on sub-block characteristics such as color channel content, texture complexity, and frequency distribution. This adaptive parameter adjustment reduces storage requirements while keeping the system complexity manageable through rule-based or machine learning-driven parameter selection.
4Quantity of substance
If high compression rates are applied to reduce storage, then storage requirements are reduced, but image quality and distortion efficiency deteriorate
Solution Approach 1:
Different sub-blocks are compressed with different quality levels based on their visual importance and characteristics. Regions with high visual importance maintain higher quality with lower compression, while less critical regions accept higher compression rates, optimizing the overall balance between storage requirements and image quality.
Solution Approach 2:
Compression parameters such as quantization levels, transform block sizes, and prediction modes are dynamically adjusted based on sub-block characteristics to achieve optimal compression rates while maintaining acceptable image quality. This localized parameter optimization prevents excessive quality loss in important regions while achieving high compression in tolerant regions.
Data Source
AI summary
Disclosed herein includes a system, a method, and a device for compressing image data. The device includes one or more processors, coupled to memory, configured to identify a plurality of sub-blocks of a block of image data including a first sub-block and a second sub-block. The one or more processors are configured to identify a first data characteristic of data of the first sub-block and a second data characteristic of data of the second sub-block, determine a first compression technique based at least on the first data characteristic of the first sub-block, determine a second compression technique based at least on the second data characteristic of the second sub-block, and compress the first sub-block using the first compression technique and the second sub-block using the second compression technique.