Image Compression Prediction Model for Multi-Bitrate Residual Generation
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Solution Overview
Problem
Existing image compression technologies face challenges in efficiently predicting and compressing target sets of pixels across various bitrates, leading to suboptimal compression efficiency and quality.
Innovation Solution
A computer-implemented method that generates a residual between a refined set of pixels and a target set of pixels, using a prediction model to estimate the target set of pixels and a compression model to handle multiple bitrates by training with various quantization step sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a prediction model is used to estimate target pixels, then compression efficiency is improved, but manufacturing precision of pixel prediction deteriorates
Solution Approach 1:
A prediction model is introduced as an intermediary component that estimates target pixels before final encoding. The model takes reference pixels and motion information as input, generates predicted pixels, and the difference (residual) is then encoded. This intermediary prediction step enables compression efficiency improvement while maintaining acceptable prediction accuracy through learned patterns.
Solution Approach 2:
The system changes parameters dynamically by adjusting quantization step sizes based on rate-distortion tradeoffs. Different quantization parameters are applied to different regions or blocks, allowing the system to optimize between compression efficiency and prediction accuracy depending on the specific content and desired bitrate.
2Adaptability or versatility
If multiple quantization step sizes are used for different bitrates, then adaptability is improved, but device complexity increases
Solution Approach 1:
A single prediction model is designed to perform multiple functions across different bitrates and quantization step sizes. The model structure remains universal while adapting to different operating conditions through parameter adjustments rather than requiring separate models for each bitrate, thus improving adaptability without proportionally increasing complexity.
Solution Approach 2:
The system dynamically adjusts quantization step sizes and model parameters based on the target bitrate and rate-distortion tradeoffs. This dynamic adaptation allows the same model to efficiently handle multiple bitrates by changing operational parameters rather than structural complexity.
Data Source
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AI summary
Systems and methods for predicting a target set of pixels are disclosed. In one embodiment, a method may include obtaining target content. The target content may include a target set of pixels to be predicted. The method may also include convolving the target set of pixels to generate an estimated set of pixels. The method may include matching a second set of pixels in the target content to the target set of pixels. The second set of pixels may be within a distance from the target set of pixels. The method may include refining the estimated set of pixels to generate a refined set of pixels using a second set of pixels in the target content.