Video Encoder Quantization Selection Among RDOQ, SQ, and HDQ
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Solution Overview
Problem
Existing quantization methods for encoding high-resolution video content face challenges in achieving high image quality with a small amount of computation, as operations like RDOQ require significant computational resources, while other methods like SQ and HDQ may compromise image quality.
Innovation Solution
An encoder that selects an efficient quantization operation using a machine learning model to determine the appropriate quantization method based on feature values, allowing for improved image quality with reduced computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If RDOQ operation is used for quantization, then image quality is improved, but computational resources increase significantly
Solution Approach 1:
The encoder dynamically selects between different quantization operations (RDOQ, SQ, HDQ) based on the characteristics of the input data block. A machine learning model analyzes features such as transform coefficient distributions and determines the most appropriate quantization method for each block, allowing the system to adapt its computational approach rather than applying a fixed method universally.
Solution Approach 2:
Different quantization operations are applied to different blocks of image data based on their local characteristics. Blocks with specific features (e.g., high frequency content, large coefficient values) may receive RDOQ processing while other blocks use simpler methods, ensuring that computational resources are concentrated where they provide the most benefit to overall image quality.
2Use of energy by moving object
If simpler quantization methods like SQ or HDQ are used, then computational resources are reduced, but image quality deteriorates
Solution Approach 1:
The system changes the quantization parameters and methods based on the statistical properties of the input data. By analyzing features such as the distribution of transform coefficients, variance, and other characteristics, the machine learning model selects quantization parameters that optimize the balance between computational efficiency and image quality for each specific data block.
Solution Approach 2:
The quantization strategy transitions from static to dynamic, where the encoder continuously evaluates data characteristics and adjusts the quantization method accordingly. This dynamic adaptation ensures that simpler methods are used only when appropriate, while more sophisticated methods are applied when they will genuinely improve image quality.
Data Source
AI summary
An encoder and a video processing system including the encoder are provided. The encoder may include a quantization operation selection circuit configured to generate first data by performing a first quantization operation based on input data including image data of a frequency domain, to generate a feature value based on the first data, and to generate a control signal for selecting a second quantization operation or a rate-distortion optimized quantization (RDOQ) operation based on a machine learning model trained based on training data that includes the feature value. Additionally, the encoder may include a quantizer configured to perform the second quantization operation or the RDOQ operation based on the control signal and to generate output data (e.g., after performing the second quantization operation or the RDOQ operation).