Neural Network Video Compression Adaptive Bit Allocation
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Solution Overview
Problem
Neural network-based video compression faces challenges such as sub-optimal bit allocation, inadequate use of reference frames for prediction, lack of spatial bit allocation, and inefficient residual coding, leading to subpar rate-distortion performance.
Innovation Solution
Implement adaptive bit allocation using scaled lambda values for different temporal and spatial regions within a video frame, leveraging multiple reference frames for improved prediction and incorporating interpolated frames for enhanced residual coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform bit allocation is used across all temporal layers, then the implementation is simple, but the rate-distortion performance is sub-optimal
Solution Approach 1:
The patent applies local quality by assigning different lambda values to different temporal layers based on their specific rate-distortion characteristics. Each temporal layer receives a customized lambda value (lambda_0, lambda_1, lambda_2, etc.) rather than a uniform value, allowing optimal bit allocation tailored to the specific quality requirements and distortion characteristics of each layer.
Solution Approach 2:
The patent implements dynamics by making the lambda values adaptable and adjustable for each temporal layer. The system dynamically selects appropriate lambda values based on the rate-distortion function characteristics of each temporal layer, enabling flexible optimization of bit allocation across different layers rather than using a static uniform approach.
2Device complexity
If only two reference frames are used for prediction, then the processing complexity is low, but the prediction accuracy is insufficient
Solution Approach 1:
The patent applies another dimension by extending the reference frame utilization from the conventional two reference frames to multiple reference frames (N > 2). This dimensional expansion in the temporal reference space allows the system to leverage additional historical and future frame information, significantly improving prediction accuracy for temporal layers while managing complexity through structured multi-frame processing.
3Device complexity
If no spatial bit allocation is implemented, then the system is simpler, but the compression efficiency is sub-optimal
Solution Approach 1:
The patent applies local quality at the spatial level by implementing region-specific lambda values (lambda_R, lambda_G, lambda_B for different regions or channels). This allows the system to allocate bits differently across spatial regions based on their specific rate-distortion characteristics, optimizing compression efficiency for each region rather than using a uniform allocation approach.
4Device complexity
If residual coding does not use interpolated frames, then the coding process is simpler, but the rate-distortion performance is sub-optimal
Solution Approach 1:
The patent applies preliminary action by generating interpolated frames before the residual coding process. The interpolated frames are created from surrounding frames and used as additional references during residual coding, allowing the system to leverage temporal interpolation information to improve prediction accuracy and reduce distortion in the residual signal.
Data Source
AI summary
A method of video processing includes determining a target bitrate for a current video unit of a video that is based on a rate distortion function in which a rate portion is weighted using lambda, wherein lambda is a rational number and wherein lambda is an adaptively adjusted for each video unit of the video and performing a conversion between the current video unit and a bitstream of the video.