18526370. METHOD AND DEVICE FOR PROCESSING VIDEO SIGNAL BY USING CROSS-COMPONENT LINEAR MODEL simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 METHOD AND DEVICE FOR PROCESSING VIDEO SIGNAL BY USING CROSS-COMPONENT LINEAR MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND DEVICE FOR PROCESSING VIDEO SIGNAL BY USING CROSS-COMPONENT LINEAR MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does this technology compare to existing video signal processing methods in terms of efficiency and performance?
- 1.11 What are the specific technical requirements for implementing this video signal processing method in real-world applications?
- 1.12 Original Abstract Submitted
METHOD AND DEVICE FOR PROCESSING VIDEO SIGNAL BY USING CROSS-COMPONENT LINEAR MODEL
Organization Name
Inventor(s)
Dongcheol Kim of Suwon-Si (KR)
METHOD AND DEVICE FOR PROCESSING VIDEO SIGNAL BY USING CROSS-COMPONENT LINEAR MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18526370 titled 'METHOD AND DEVICE FOR PROCESSING VIDEO SIGNAL BY USING CROSS-COMPONENT LINEAR MODEL
Simplified Explanation
The video signal processing method described in the abstract involves various steps such as downsampling luma components, acquiring maximum and minimum luma values, and calculating average luma values based on these acquired values.
- Downsampling luma components of reconstructed blocks adjacent to a current block
- Acquiring a maximum luma value based on a first index
- Acquiring a next-highest maximum luma value based on a second index
- Acquiring a maximum average luma value based on the average value of the maximum and next-highest maximum luma values
- Acquiring a next-lowest minimum luma value based on a third index
- Acquiring a minimum luma value based on a fourth index
- Acquiring a minimum average luma value based on the next-lowest minimum luma value and the minimum luma value
Potential Applications
This technology can be applied in video processing, image enhancement, and quality improvement in multimedia applications.
Problems Solved
This technology helps in optimizing luma values in video signals, leading to better image quality and enhanced visual experience for viewers.
Benefits
The benefits of this technology include improved video quality, enhanced image sharpness, and better overall visual performance in multimedia content.
Potential Commercial Applications
Potential commercial applications of this technology include video streaming services, video production companies, multimedia content creators, and display technology manufacturers.
Possible Prior Art
One possible prior art in this field could be existing video processing algorithms and methods used in the industry.
Unanswered Questions
How does this technology compare to existing video signal processing methods in terms of efficiency and performance?
The article does not provide a direct comparison with existing methods, leaving a gap in understanding the specific advantages of this technology over others.
What are the specific technical requirements for implementing this video signal processing method in real-world applications?
The article does not detail the specific technical requirements or hardware/software dependencies needed for the implementation of this technology, leaving a gap in practical information for potential users or developers.
Original Abstract Submitted
The video signal processing method comprises the steps of: downsampling luma components of reconstructed blocks adjacent to a current block; acquiring a maximum luma value from among the downsampled luma components based on a first index; acquiring a next-highest maximum luma value from among the downsampled luma components based on a second index; acquiring a maximum average luma value based on an average value of the maximum luma value and the next-highest maximum luma value; acquiring a next-lowest minimum luma value from among the downsampled luma components based on a third index; acquiring a minimum luma value from among the downsampled luma components based on a fourth index; and acquiring a minimum average luma value based on the next-lowest minimum luma value and the minimum luma value.
