Intel corporation (20240348801). ADAPTIVE GOP SIZE SELECTION simplified abstract
Contents
ADAPTIVE GOP SIZE SELECTION
Organization Name
Inventor(s)
Sebastian Possos of Sammamish WA (US)
Yi-jen Chiu of San Jose CA (US)
Ximin Zhang of San Jose CA (US)
ADAPTIVE GOP SIZE SELECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240348801 titled 'ADAPTIVE GOP SIZE SELECTION
Simplified Explanation: The patent application addresses the limitation of using a fixed group of pictures (GOP) size in video encoding, which hinders compression efficiency due to its inability to adapt to the dynamic nature of video content.
- **Key Features and Innovation:**
- The innovation involves a GOP size recommendation engine using machine learning models to determine frame-level GOP size recommendations based on pre-encoder frame statistics. - Frame-level GOP size recommendations are used to adapt the GOP size for encoding video frames, improving compression efficiency.
- **Potential Applications:**
- Video streaming services - Video surveillance systems - Video conferencing platforms
- **Problems Solved:**
- Inefficient compression due to fixed GOP size - Visual artifacts in high-motion areas - Wasted bits in low-motion segments
- **Benefits:**
- Improved compression efficiency - Reduced visual artifacts - Enhanced video quality
- **Commercial Applications:**
- Optimizing video encoding processes for various industries - Enhancing user experience in video streaming services
- **Prior Art:**
- Prior research on adaptive GOP sizes in video encoding - Studies on machine learning applications in video compression
- **Frequently Updated Research:**
- Advances in machine learning algorithms for video encoding - Studies on optimizing compression efficiency in video processing
Questions about video encoding: 1. How does the use of machine learning models improve compression efficiency in video encoding? 2. What are the potential challenges in implementing frame-level GOP size recommendations in video encoding systems?
Original Abstract Submitted
using a fixed group of pictures (gop) size in video encoding significantly hinders compression efficiency due to its inability to adapt to the dynamic nature of video content. while encoding leverages spatio-temporal redundancy within a gop for compression, a predetermined size fails to capture the varying complexity of scenes. this leads to wasted bits in low-motion segments and insufficient reference frame variation for high-motion areas, resulting in visual artifacts and reduced compression efficiency. to address this limitation, a gop size recommendation engine involving machine learning models can determine frame-level gop size recommendations based on pre-encoder frame statistics. the frame-level gop size recommendations are used to adapt the gop size for encoding video frames.