Image Encoding Adaptive Quantization for Bitrate-Quality Control
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Solution Overview
Problem
Existing image encoding and decoding technologies face challenges in determining the quantization parameter to balance the quality of the reconstructed image with the number of coded bits, leading to inefficiencies in bit rate control.
Innovation Solution
An image encoding and decoding method that dynamically sets the quantization parameter based on the image content and bitstream buffer conditions to optimize the number of coded bits while maintaining image quality, considering factors like complexity and buffer fullness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a smaller quantization parameter is used, then the quality of the reconstructed image is improved, but the number of bits in the bitstream increases
Solution Approach 1:
The patent implements dynamic rate control by continuously monitoring the bitstream buffer fullness and adjusting the quantization parameter in real-time during encoding. The encoder dynamically selects QP values based on current buffer conditions and image content complexity, transitioning from static to adaptive quantization to optimize the trade-off between image quality and bitstream size.
Solution Approach 2:
The patent changes the quantization parameter based on multiple factors including bitstream buffer fullness, image content complexity, and coding unit characteristics. By varying the QP parameter dynamically according to these conditions, the system adapts the compression level to achieve optimal balance between reconstruction quality and bit rate efficiency.
2Quantity of substance
If a larger quantization parameter is used, then the number of bits in the bitstream is reduced, but the quality of the reconstructed image deteriorates
Solution Approach 1:
The patent applies different quantization parameters to different coding units based on their local image content characteristics. Complex regions (e.g., edges, textures) receive smaller QP values to preserve detail, while simple regions receive larger QP values for greater compression, achieving spatially adaptive quality control.
Solution Approach 2:
The patent implements feedback-based rate control where the encoder monitors the bitstream buffer fullness and adjusts subsequent quantization decisions based on this feedback. The system uses the buffer status as feedback to dynamically modulate the QP parameter, creating a closed-loop control system that prevents buffer overflow or underflow.
3Device complexity
If the quantization parameter is determined without considering bitstream buffer conditions, then the encoding process is simpler, but the bit rate control accuracy is insufficient
Solution Approach 1:
The patent performs preliminary assessment of image content complexity and estimates the bit cost for different coding options before final encoding decisions. By pre-evaluating the information amount and complexity of coding units, the system prepares advance guidance for quantization parameter selection, improving control accuracy without excessive runtime complexity.
Data Source
AI summary
An image encoding and decoding 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, two factors: image content of the coding unit and a number of bits of data in a bitstream buffer are considered, and a target number of bits is dynamically set. At an encoder side, the coding unit is encoded based on a quantization parameter determined based on the target number of bits. At a decoder side, the bitstream of the coding unit is decoded based on the quantization parameter determined based on the target number of bits.