18622378. METHOD, DEVICE, AND MEDIUM FOR VIDEO PROCESSING simplified abstract (Bytedance Inc.)
Contents
METHOD, DEVICE, AND MEDIUM FOR VIDEO PROCESSING
Organization Name
Inventor(s)
Kai Zhang of Los Angeles CA (US)
Li Zhang of Los Angeles CA (US)
METHOD, DEVICE, AND MEDIUM FOR VIDEO PROCESSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18622378 titled 'METHOD, DEVICE, AND MEDIUM FOR VIDEO PROCESSING
The abstract describes a method for video processing that involves selecting a machine learning model and applying it to convert video blocks into a bitstream.
- Simplified Explanation:
This patent application proposes a method for video processing that involves selecting a machine learning model and applying it to convert video blocks into a bitstream.
- Key Features and Innovation:
- Obtaining granularities for selecting and applying machine learning models - Converting video blocks into a bitstream based on the selected granularities
- Potential Applications:
- Video streaming services - Video editing software - Surveillance systems
- Problems Solved:
- Efficient video processing - Improved video quality - Enhanced data compression
- Benefits:
- Faster video processing - Higher quality video output - Reduced storage space for videos
- Commercial Applications:
- Optimizing video streaming services - Enhancing video editing capabilities - Improving surveillance system efficiency
- Questions about Video Processing:
1. How does the method of selecting granularities improve video processing efficiency? 2. What are the potential drawbacks of using machine learning models for video conversion?
- Frequently Updated Research:
- Stay updated on advancements in machine learning models for video processing to enhance the efficiency and quality of video conversion.
Original Abstract Submitted
Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: obtaining a first granularity of selection of a machine learning model for processing a video and a second granularity of applying the machine learning model; and performing, based on the first and second granularities, a conversion between a current video block of the video and a bitstream of the video.
