Deep Learning In-Loop Filtering for QP-Adaptive Inter Prediction
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Solution Overview
Problem
Existing video compression techniques struggle with varying levels of image distortion in P-frames and B-frames due to changing quantization parameter (QP) values, necessitating an adaptive in-loop filter to improve coding efficiency.
Innovation Solution
A deep learning-based in-loop filter is employed to mitigate image distortion by using an embedding vector calculated from the quantization parameter and a denoising model to enhance frames, specifically for P-frames and B-frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional in-loop filters are used for inter-prediction, then device complexity is low, but image quality deteriorates due to varying distortion levels from different QP values
Solution Approach 1:
The patent implements a dynamic filter selection mechanism where the in-loop filter adapts its operation based on the QP value of the current frame. The filter is selectively applied or modified according to the quantization parameter, allowing the system to optimize image quality for different compression levels without requiring a completely complex filter architecture for all scenarios.
Solution Approach 2:
The patent changes the operational parameters of the in-loop filter based on the QP value. By monitoring the quantization parameter and adjusting filter strength, type, or activation status accordingly, the system achieves adaptive image quality improvement without maintaining a permanently complex filter structure, thus resolving the contradiction between quality and complexity.
2Adaptability or versatility
If a fixed in-loop filter is used, then device complexity is low, but adaptability to different QP values deteriorates
Solution Approach 1:
The filter system transitions from a static, fixed configuration to a dynamic one that responds to QP value changes. The filter's behavior is adjusted in real-time based on the quantization parameter of each frame, enabling adaptability without requiring multiple separate filter systems, thus balancing versatility and complexity.
Solution Approach 2:
The in-loop filter system uses the QP value information already present in the encoding process to automatically adjust its own operation. This self-service mechanism allows the filter to adapt to different compression conditions without external control complexity, achieving versatility through autonomous parameter-based adjustment.
3Productivity
If deep learning-based in-loop filter is applied, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent integrates the deep learning-based in-loop filter into the decoding process itself, performing quality enhancement before the reconstructed frame is used for subsequent inter-prediction. This preliminary action ensures that reference frames used for prediction are already enhanced, improving coding efficiency without requiring separate post-processing steps, thus managing complexity through integration rather than addition.
Solution Approach 2:
The deep learning filter acts as an intermediary component between the conventional decoding process and the inter-prediction process. By positioning the complex filter as a mediator that processes frames between reconstruction and prediction, the system achieves coding efficiency improvements while containing complexity within a specific functional layer rather than propagating it throughout the entire system.
Data Source
AI summary
A method and an apparatus for video coding using a deep learning-based in-loop filter for inter-prediction are disclosed. The video coding method and the apparatus utilize a deep learning-based in-loop filter for inter-prediction of a predictive frame (P-frame) and a bipredictive frame (B-frame) in order to mitigate various levels of image distortion according to a QP (quantization parameter) value present in the P-frame and the B-frame.