Coding Unit Quantization Control for Accurate Bit Rate Encoding
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Solution Overview
Problem
Existing image encoding and decoding methods suffer from poor quality of reconstructed images due to inaccurate determination of quantization parameters, which are based on empirical data and do not consider the image content, leading to inefficient bit rate control.
Innovation Solution
The proposed methods determine a quantization parameter by considering the image content, number of lossy bits, and bitstream buffer fullness to dynamically set a target number of bits for each coding unit, ensuring accurate bit rate control and improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a smaller quantization parameter is used to improve reconstructed image quality, then image quality improves, but the number of bits in the bitstream increases
Solution Approach 1:
The patent applies local quality by dividing the image into multiple coding units and assigning different quantization parameters to different coding units based on their local complexity characteristics. Simple regions use larger QP values for compression, while complex regions use smaller QP values for quality preservation, achieving differential quality control across the image.
Solution Approach 2:
The patent implements dynamic quantization parameter adjustment by using rate control modules to continuously adapt QP values based on real-time buffer fullness conditions and image content complexity. The system dynamically switches between different QP strategies (intra-frame vs inter-frame, different complexity levels) to optimize the balance between quality and bit rate.
2Ease of operation
If empirical data-based fullness ranges are used for rate control, then the encoding process is simple, but the quantization parameter accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the rate control module continuously monitors buffer fullness conditions and uses this feedback to adjust quantization parameters. The system measures actual encoding results and feeds this information back to modify subsequent QP decisions, creating a closed-loop control system that improves accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent changes the parameter determination approach from static empirical ranges to dynamic parameter adjustment based on multiple factors including buffer fullness, image content complexity, and encoding mode. The system transitions from fixed QP tables to adaptive QP calculation that responds to real-time conditions, improving precision without significantly increasing complexity.
3Manufacturing precision
If more redundant information is included in the bitstream to improve image quality, then reconstructed image quality improves, but the bitstream size increases
Solution Approach 1:
The patent segments the image into multiple coding units and applies different compression strategies to each segment based on its characteristics. This allows the system to include more redundant information (maintain quality) in complex regions while applying more aggressive compression in simple regions, optimizing the overall quality-to-size ratio of the bitstream.
Data Source
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AI summary
Image encoding and decoding methods and apparatuses, an encoder, a decoder, and a system are disclosed, and relate to the field of multimedia technologies. The method includes: When a to-be-decoded bitstream of a coding unit in an image bitstream is decoded, or a to-be-encoded coding unit in a current frame is encoded, three factors are considered: image content of the coding unit, a number of lossy bits, and a buffer fullness of a bitstream buffer, and a target number of bits is dynamically set. The encoder encodes the coding unit based on a quantization parameter determined based on the target number of bits. The decoder decodes the bitstream of the coding unit based on the quantization parameter determined based on the target number of bits. This helps improve accuracy of bit rate control, and minimizes a number of coded bits obtained by encoding an image while ensuring quality of a reconstructed image.