Qualcomm incorporated (20240137524). BIT-LENGTH CONTROL FOR LINEAR REGRESSION-BASED AFFINE MERGE CANDIDATE DERIVATION simplified abstract
Contents
- 1 BIT-LENGTH CONTROL FOR LINEAR REGRESSION-BASED AFFINE MERGE CANDIDATE DERIVATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 BIT-LENGTH CONTROL FOR LINEAR REGRESSION-BASED AFFINE MERGE CANDIDATE DERIVATION - 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 Original Abstract Submitted
BIT-LENGTH CONTROL FOR LINEAR REGRESSION-BASED AFFINE MERGE CANDIDATE DERIVATION
Organization Name
Inventor(s)
Yan Zhang of San Diego CA (US)
Han Huang of San Diego CA (US)
Vadim Seregin of San Diego CA (US)
Marta Karczewicz of San Diego CA (US)
BIT-LENGTH CONTROL FOR LINEAR REGRESSION-BASED AFFINE MERGE CANDIDATE DERIVATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240137524 titled 'BIT-LENGTH CONTROL FOR LINEAR REGRESSION-BASED AFFINE MERGE CANDIDATE DERIVATION
Simplified Explanation
The patent application describes a device for coding video data that includes reducing the bit length of input variables for a linear regression operation to generate reduced bit length input variables, performing the linear regression operation to derive an affine motion model, and coding a current block of video data based on the affine motion model.
- Memory to store video data
- Processors to reduce bit length of input variables
- Linear regression operation to generate reduced bit length input variables
- Deriving an affine motion model
- Coding video data based on the affine motion model
Potential Applications
This technology could be applied in video compression algorithms, video streaming services, and video editing software.
Problems Solved
This technology solves the problem of efficiently coding video data by reducing the bit length of input variables and deriving an affine motion model for coding blocks of video data.
Benefits
The benefits of this technology include improved video compression efficiency, reduced storage requirements for video data, and enhanced video quality during transmission and playback.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of advanced video coding systems for streaming services, video surveillance systems, and virtual reality applications.
Possible Prior Art
One possible prior art for this technology could be existing video coding algorithms that use linear regression for motion estimation and compensation in video compression techniques.
Unanswered Questions
How does this technology compare to existing video coding algorithms in terms of compression efficiency and video quality?
This article does not provide a direct comparison between this technology and existing video coding algorithms in terms of compression efficiency and video quality.
What are the potential limitations or challenges in implementing this technology in real-world video coding systems?
This article does not address the potential limitations or challenges in implementing this technology in real-world video coding systems.
Original Abstract Submitted
an example device for coding video data includes memory configured to store the video data and one or more processors communicatively coupled to the memory. the one or more processors are configured to reduce a bit length of one or more input variables for a linear regression operation to generate one or more reduced bit length input variables, the input variables including at least one of a) one or more delta coordinates, b) one or more delta motion vectors, or c) a value representing a number of subblocks. the one or more processors are configured to perform the linear regression operation and derive an affine motion model based on the performing the linear regression on the one or more reduced bit length input variables. the one or more processors are configured to code a current block of the video data based on the affine motion model.
