Image Encoding Bit Allocation by Area Complexity
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Solution Overview
Problem
Existing electronic apparatuses face challenges in encoding image content data at a target bit rate while maintaining image quality, as the varying data size of image frames makes it difficult to meet the target bit rate without compromising image quality.
Innovation Solution
The apparatus segments image frames into areas based on complexity, assigning different bit numbers to each area according to its frequency characteristics, and uses a processor to encode data using a quantization parameter inversely proportional to the bit number, ensuring that areas with higher complexity receive more bits to maintain image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a constant bit rate is used to encode image content data, then the data transfer bandwidth is controlled within a limit, but the image quality deteriorates when the data size after compression does not reach the target compression rate
Solution Approach 1:
The patent applies local quality by dividing the image frame into multiple regions with different complexity levels and assigning different bit rates to each region. High-complexity regions (with more detail and frequency components) receive higher bit rates to preserve quality, while low-complexity regions receive lower bit rates. This resolves the contradiction by ensuring that critical areas maintain high quality even under constant bit rate constraints.
Solution Approach 2:
The patent segments the image frame into multiple regions based on complexity characteristics before encoding. By segmenting the image and applying different encoding parameters to each segment, the system can control the overall bit rate while preserving quality in important regions, thus resolving the contradiction between data size control and image quality maintenance.
2Device complexity
If the compression rate is set for the whole image content data, then the encoding process is simplified, but it is not easy to meet the target bit rate because image frames have different data sizes after compression according to image characteristics
Solution Approach 1:
Instead of applying a uniform compression rate to the entire image, the patent uses local quality by determining different bit rates for different regions based on their complexity. This allows the encoding process to meet the target bit rate more accurately by adapting to local image characteristics, resolving the contradiction between encoding simplicity and bit rate compliance.
3Manufacturing precision
If more bits are allocated to areas with higher complexity, then the image quality in those areas is maintained, but the overall data size increases making it difficult to meet the target bit rate
Solution Approach 1:
The patent resolves this contradiction by applying local quality - allocating more bits only to high-complexity regions that require it for quality preservation, while using fewer bits in low-complexity regions. This selective bit allocation maintains overall image quality where needed while controlling the total data size to meet the target bit rate.
Solution Approach 2:
The patent changes the bit rate parameter dynamically based on regional complexity characteristics. By adjusting the bit rate parameter according to local image properties rather than using a fixed value, the system can maintain quality in critical areas while controlling overall data size to comply with the target bit rate.
Data Source
AI summary
An electronic apparatus includes: an interface configured to communicate with an external apparatus; and a processor configured to encode data of image content with a bit number based on complexity of an image in an area according to areas of frames of the image content, and control the interface to transmit the encoded data to the external apparatus.