Adaptive Quantization for Distributed Video Encoding
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Solution Overview
Problem
Existing video coding techniques, such as Wyner-Ziv coding, face challenges in maintaining coding performance due to varying statistical characteristics across different areas of an image, leading to inefficient bit allocation and increased computational complexity.
Innovation Solution
Implementing adaptive quantization in distributed video encoding and decoding, where quantization strength is determined for each image area, allowing for efficient bit allocation and improved bit rate-distortion performance by filling quantization discard positions with predefined bits, enabling the use of fixed-length channel codes and reducing the need for additional parity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform quantization is applied across all image areas, then device complexity is reduced, but bit rate-distortion performance deteriorates due to varying statistical characteristics in different areas
Solution Approach 1:
The patent applies different quantization strengths to different image areas based on their statistical characteristics. The image is divided into multiple areas, and each area is quantized with a strength adapted to its local properties, thereby improving bit rate-distortion performance without significantly increasing overall device complexity.
Solution Approach 2:
The patent segments the image into multiple areas for differential quantization processing. By dividing the image and applying area-specific quantization parameters, the system achieves better compression performance while managing complexity through structured segmentation.
2Manufacturing precision
If adaptive quantization with variable bit lengths is used, then bit rate-distortion performance improves, but channel coding complexity increases and fixed-length code usage becomes difficult
Solution Approach 1:
The patent performs preliminary padding of quantized coefficients with predetermined bits before channel coding. This pre-processing step ensures that the input to the channel coder has a fixed length, enabling the use of fixed-length codes and simplifying the channel coding process while still allowing adaptive quantization to improve bit rate-distortion performance.
Solution Approach 2:
The patent changes the parameter representation by padding variable-length quantized data with predetermined bits to create fixed-length output. This parameter transformation allows the system to benefit from adaptive quantization while maintaining compatibility with fixed-length channel codes, thereby reducing channel coding complexity.
3Productivity
If strong quantization is applied to reduce bit rate, then transmission efficiency improves, but image quality deteriorates
Solution Approach 1:
The patent applies different quantization strengths to different image areas, using stronger quantization in less important areas to reduce bit rate and weaker quantization in important areas to maintain image quality. This localized approach optimizes the trade-off between transmission efficiency and image quality.
Data Source
AI summary
A distributed video encoder and decoder and a distributed video decoding method using adaptive quantization are provided. Adaptive quantization is performed at the time of encoding and decoding so that limited resources and information can be efficiently used, and a predetermined bit which is previously defined is included in a position of a bit which does not need to be transmitted for channel coding, thereby improving a bit rate-distortion performance as a whole.