Video Codec Deep Learning Super Block YUV Processing
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Solution Overview
Problem
Current video compression techniques, such as H.264/AVC, HEVC, and VVC, face challenges in achieving higher coding efficiency and image enhancement due to increasing image sizes, resolutions, and frame rates, leading to increased data requirements and hardware resource utilization.
Innovation Solution
A video codec that processes video blocks using a deep learning model by generating a super block by stacking or packing YUV video blocks and performing convolutional operations based on the characteristics of these blocks, improving coding efficiency and reducing memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video compression techniques (H.264/AVC, HEVC, VVC) are used, then video data can be compressed and transmitted, but coding efficiency is insufficient and hardware resource requirements increase with higher resolutions and frame rates
Solution Approach 1:
The video block is divided into separate Y, U, and V component blocks that are processed independently through the deep learning model. This segmentation allows the model to handle different color components separately, improving coding efficiency while managing computational complexity through specialized processing of each component.
Solution Approach 2:
The Y, U, and V blocks are stacked in the channel dimension to form a multi-channel input tensor for the deep learning model. This dimensional transformation enables the model to process color information across multiple channels simultaneously, achieving superior compression efficiency compared to conventional single-channel processing.
2Manufacturing precision
If image size, resolution, and frame rate are increased, then video quality is improved, but the amount of data to be encoded increases
Solution Approach 1:
The deep learning model learns optimal transformation parameters and compression strategies through training on high-resolution video data. By adjusting model parameters during training, the system achieves efficient compression of high-quality video while adapting to different resolutions and frame rates, reducing data amount without sacrificing video quality.
3Productivity
If deep learning-based image processing techniques are applied to existing encoding techniques, then coding efficiency is improved, but model processing capability must be enhanced to handle complex super blocks
Solution Approach 1:
The complex video block is segmented into Y, U, and V component blocks that are processed separately and then combined. This segmentation reduces the complexity of the input to the deep learning model, allowing it to achieve high coding efficiency without requiring excessive model capacity, thus balancing processing capability requirements with performance gains.
Data Source
AI summary
A video codec uses a block-based deep learning model. The video codec, when processing video blocks by using a deep learning model, generates a super block by stacking or packing the respective ones of YUV video blocks and inputs the generated super block to the deep learning model. The video codec processes the inputs differently in the course of performing convolution within the deep learning model, according to the characteristics of the constituent YUV blocks of the super block