Video Encoder Chrominance Prediction Using Luminance Gradients
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Solution Overview
Problem
Current video coding technologies, such as HEVC, face challenges in efficiently encoding and decoding video frames due to high processing loads and complex configurations, particularly in generating chrominance prediction images from luminance prediction images, which can lead to increased computational requirements and potential decreases in prediction accuracy.
Innovation Solution
The proposed solution involves an encoder and decoder that generate luminance and chrominance final prediction images using luminance gradient values and local motion estimation values, bypassing the need to derive chrominance gradient values and local motion estimation values directly from chrominance, thereby simplifying the encoding and decoding processes and reducing processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chrominance gradient values and local motion estimation values are derived directly from chrominance, then prediction accuracy for chrominance images is improved, but processing load and device complexity increase
Solution Approach 1:
The patent uses luminance gradient values and luminance local motion estimation values as intermediaries to generate chrominance prediction images. Instead of directly processing chrominance data to obtain gradient and motion estimation values, the invention leverages the already-processed luminance parameters as a mediator, thereby reducing the processing load while maintaining prediction accuracy through the correlation between luminance and chrominance components.
Solution Approach 2:
The luminance gradient values and luminance local motion estimation values serve multiple functions: they are used both for generating luminance prediction images and for generating chrominance prediction images. This multi-functionality reduces redundant calculations and simplifies the overall processing architecture while maintaining prediction accuracy for both luminance and chrominance components.
2Measurement precision
If complex inter frame prediction processes are used to generate chrominance prediction images, then prediction accuracy is improved, but encoding and decoding complexity increase
Solution Approach 1:
The patent merges the generation processes of luminance and chrominance prediction images by using the same luminance gradient values and luminance local motion estimation values for both components. This combining of processes reduces encoding and decoding complexity while maintaining prediction accuracy, as the correlated nature of luminance and chrominance allows shared processing steps.
Solution Approach 2:
The invention copies the luminance gradient values and luminance local motion estimation values to be used for chrominance prediction image generation. Instead of independently computing these parameters for chrominance, the patent copies the already-computed luminance parameters, thereby reducing complexity while preserving prediction accuracy through the inherent correlation between luminance and chrominance.
3Measurement precision
If separate processing paths are used for luminance and chrominance prediction, then prediction accuracy is maintained, but processing time and productivity decrease
Solution Approach 1:
The patent performs preliminary processing of luminance data to obtain gradient values and local motion estimation values before chrominance processing is needed. These pre-computed luminance parameters are then reused for chrominance prediction, eliminating the need to perform separate gradient and motion estimation calculations for chrominance, thereby improving processing speed while maintaining prediction accuracy.
Data Source
AI summary
Provided is an encoder that achieves further improvement. The encoder includes processing circuitry and memory. Using the memory, the processing circuitry: obtains two prediction images from two reference pictures; derives a luminance gradient value of each pixel position in each of the two prediction images; derives a luminance local motion estimation value of each pixel position in a current block; generates a luminance final prediction image using a luminance value and the luminance gradient value in each of the two prediction images, and the luminance local motion estimation value of the current block; and generates a chrominance final prediction image using at least one of the luminance gradient value of each of the two prediction images or the luminance local motion estimation value of the current block, and chrominance of each of the two prediction images.