Intel corporation (20240348801). ADAPTIVE GOP SIZE SELECTION simplified abstract

ADAPTIVE GOP SIZE SELECTION

Organization Name

intel corporation

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.