CCCM Prediction With Adaptive Tap Limits for Hardware Video Coding
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Solution Overview
Problem
Conventional video compression schemes, particularly convolutional cross-component model (CCCM) prediction, suffer from high computational latency due to the use of a large number of taps, making hardware implementations impractical.
Innovation Solution
Implement tap-constrained convolutional cross-component model prediction by limiting the number of taps based on block size or downsampling, using prediction models with specific tap configurations, and encoding these constraints in the bitstream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of taps are used in convolutional cross-component model prediction, then prediction accuracy is improved, but computational latency increases making hardware implementations impractical
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the number of taps based on block size and downsampling factors. Different tap configurations (3, 4, or 5 taps) are selected according to specific conditions, optimizing the balance between prediction accuracy and computational latency for different video content characteristics
Solution Approach 2:
The patent implements dynamics by making the tap count adaptive rather than fixed. The prediction model dynamically selects the appropriate number of taps based on real-time conditions such as block size and downsampling, allowing the system to optimize performance for varying video content without sacrificing hardware implementability
2Measurement precision
If more taps are used in the prediction model, then chroma sample prediction quality is improved, but device complexity and resource intensity increase
Solution Approach 1:
The patent changes the parameter of tap count based on specific conditions (block size, downsampling). By limiting taps to maximum 5 and selecting fewer taps when appropriate, the patent reduces device complexity while maintaining prediction quality where sufficient taps are available
Solution Approach 2:
The patent segments the prediction process into different tap configurations (3-tap, 4-tap, 5-tap models) selected based on video content characteristics. This segmentation allows the system to use simpler models for certain cases and more complex models only when necessary, reducing overall device complexity
Data Source
AI summary
Tap-constrained convolutional cross-component model (CCCM) prediction enables hardware coder implementations of CCCM prediction by limiting the number of taps used to predict chroma samples while maintaining accuracy in the prediction. During encoding, a current luma sample of a block is identified. A number of taps to use for predicting a chroma sample associated with the current luma sample is determined based on a size of the block and/or whether the block is downsampled. The chroma sample is predicted using a prediction model limited to the number of taps and then encoded to an encoded bitstream. During decoding, a current luma sample of a block and a number of taps for predicting a chroma sample associated with the current luma sample are decoded from an encoded bitstream. The chroma sample is predicted using a prediction model limited to the number of taps and then output within an output video stream.