Content-Adaptive Image Compression With Blockwise Encoder Selection
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Solution Overview
Problem
Conventional image compression algorithms lack adaptability and efficiency in handling diverse image contents, as they require prior determination and are not suited for immediate changes or mixed content images.
Innovation Solution
A content adaptive compression system that includes multiple encoders (gradient, edge, and texture) and an error count unit to generate and select the best candidate compressed code based on content-specific encoding, using metrics like sum of absolute differences to determine error and select the adaptive compressed code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single compression algorithm is used for all image types, then device complexity is reduced, but compression efficiency deteriorates for diverse image contents
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently encoded by selecting the most suitable compression algorithm from multiple available algorithms. This segmentation allows different parts of the image to use different encoding methods, improving overall compression efficiency while maintaining manageable system complexity through modular processing.
Solution Approach 2:
The system dynamically selects the appropriate compression algorithm for each image block based on the content characteristics of that block. Rather than using a fixed algorithm for the entire image, the system adapts the encoding method to match the local content properties, achieving optimal compression efficiency for diverse image contents.
2Productivity
If multiple compression algorithms are prepared for different image types, then compression efficiency is improved, but adaptability to new image types deteriorates
Solution Approach 1:
The system employs a feedback mechanism where the content characteristics of each image block are analyzed, and this information feeds into the selection process of the appropriate compression algorithm. This feedback loop enables the system to adapt to new image types automatically by learning from the content properties and selecting the most suitable algorithm, rather than requiring manual configuration for each new image type.
Solution Approach 2:
The system performs self-service by automatically analyzing image content and selecting appropriate compression algorithms without external intervention. The error counting unit and mode decision unit work together to autonomously determine the best encoding method for each block, enabling the system to adapt to diverse and new image types independently.
3Speed
If compression algorithm is determined beforehand, then encoding speed is improved, but handling of mixed content images deteriorates
Solution Approach 1:
The image is segmented into multiple blocks that can be processed independently and in parallel. This segmentation allows the system to maintain high encoding speed by processing different blocks simultaneously while still applying content-adaptive algorithm selection to each block, effectively handling mixed content images without sacrificing overall encoding speed.
Solution Approach 2:
The system applies full algorithm selection and error counting only to necessary regions of the image, rather than uniformly processing the entire image with the same level of detail. This partial action approach maintains encoding speed by focusing computational resources on blocks where algorithm selection provides the most benefit, while still achieving effective handling of mixed content.
Data Source
AI summary
A content adaptive compression system includes a plurality of encoders being coupled to receive a portion of an image, and accordingly generating candidate compressed codes, respectively, the plurality of encoders being configured for encoding images of different contents. An error count unit is configured to determine an amount of error between the image and the candidate compressed code for each of the encoders. A mode decision unit is coupled to receive a plurality of the amount of error, the candidate compressed code associated with least amount of error being outputted as an adaptive compressed code for the image.