Still Image Compression via Motion Encoding Segmentation
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Solution Overview
Problem
Current image compression technologies for video cameras that record both motion and still images are inefficient for high-resolution still images, as they do not support high compression ratios without deteriorating image quality, and require separate encoders for motion and still images, leading to complex and costly apparatuses.
Innovation Solution
An image compression method that divides high-resolution still images into segments, computes their importance and relativity, and aligns them for motion image encoding using picture types (I, P, B) to achieve efficient compression, utilizing a single encoder for both image types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a motion image encoder is used for still image compression, then the apparatus configuration is simplified and cost is reduced, but the image compression capability is insufficient and image quality deteriorates
Solution Approach 1:
The still image is divided into multiple segments, which are then encoded using motion image encoding technology. This segmentation allows the motion image encoder to process still images in manageable units, resolving the contradiction between using a single encoder type and achieving adequate compression capability.
Solution Approach 2:
The patent applies different encoding strategies to different segments of the still image based on their importance. Important segments receive higher bit allocation to maintain quality, while less important segments use more aggressive compression, thus achieving good overall compression while preserving essential image quality.
2Productivity
If the still image is divided into many segments, then the compression efficiency increases, but the image quality in important portions deteriorates
Solution Approach 1:
The patent evaluates the importance of each image segment and allocates bits differently based on this evaluation. Important segments (such as those containing the main subject) receive more bits to preserve quality, while less important segments use fewer bits, thus achieving high overall compression efficiency without sacrificing critical image quality.
Solution Approach 2:
The system performs importance evaluation of image segments and uses this information to adjust bit allocation dynamically. This feedback mechanism ensures that compression resources are optimally distributed to maintain quality where it matters most while achieving high overall compression.
3Productivity
If different picture types (I, P, B) are assigned to image segments, then the overall compression ratio increases, but the complexity of encoding process increases
Solution Approach 1:
The still image is divided into multiple segments that are independently encoded with different picture types. This segmentation enables the application of diverse encoding strategies (I, P, or B pictures) to different regions, achieving high overall compression while managing complexity through modular processing.
Data Source
AI summary
When a motion image is to be compressed, the present invention divides a high-resolution still image into image segments to perform a motion image encoding process on each image segment. In this instance, the importance of each image segment and the relativity of each image segment with another image segment are computed. The image segments are then aligned in the order of importance to determine a picture type for motion image encoding in accordance with the computed relativity. Encoding is performed in accordance with a determined encoding sequence and picture type. Further, the rate control bit allocation amount for motion image encoding is increased for highly important image segments. Therefore, the present invention makes it possible to exhibit a high overall image compression efficiency while avoiding image quality deterioration in an important portion of a still image by using a motion image compression technology for still image compression.