Video Encoding Bit Rate Control via Spatial Activity Metrics
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Solution Overview
Problem
Existing video encoding methods face difficulties in controlling bit rate due to complex hardware and computational overheads, requiring multi-pass encoding and being impractical for real-time video transmission, especially at low bit rates where rate-quantization models can lead to model mismatch.
Innovation Solution
A method that uses metric functions based on AC coefficients from discrete cosine transformation data to estimate encoded video data quantity, allowing for single-pass quantization and variable length encoding, with a predictor module determining suitable quantization vectors to achieve a desired bit rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-pass encoding with rate-quantization modeling is used to control bit rate, then bit rate control accuracy is improved, but device complexity and computational overhead increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the relationship between quantization parameters and bit rates in a lookup table during an offline training phase. During actual encoding, the system simply queries this pre-computed table rather than performing complex multi-pass encoding and rate-quantization modeling, thus achieving accurate bit rate control with minimal computational overhead.
2Measurement precision
If multi-pass encoding is used to select quantization parameters, then bit rate control is improved, but encoding speed and productivity decrease
Solution Approach 1:
The patent performs the complex quantization parameter selection and bit rate calculation work in advance during an offline training phase, storing the results in a lookup table. During real-time encoding, the system only needs to query the table, enabling both accurate bit rate control and high encoding speed without requiring multi-pass encoding.
3Measurement precision
If rate-quantization modeling is used for bit rate control, then bit rate management is improved, but model mismatch occurs at low bit rates
Solution Approach 1:
The patent creates a comprehensive lookup table through offline training that captures the actual relationship between quantization parameters and bit rates across various conditions including low bit rates. This empirical model replaces theoretical rate-quantization models, eliminating model mismatch issues at low bit rates by using actual measured data from diverse video sequences.
Data Source
AI summary
A method for use in encoding video data, including generating metric values for the video data based on a metric function and respective encoding parameters. At least one of the encoding parameters is selected on the basis of a desired quantity of encoded video data and a predetermined relationship between metric values and respective quantities of encoded video data.