Dynamic Range Handling in Optical Flow Refinement
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Solution Overview
Problem
Existing video coding techniques struggle with efficiently encoding and decoding high-resolution image and video data, particularly in scenarios with limited bandwidth, due to limitations in handling dynamic range and high-dimensional inverse autocorrelation in optical flow refinement.
Innovation Solution
The proposed solution involves a method for encoding and decoding that includes dynamic range handling of high-dimensional inverse autocorrelation in optical flow refinement. This is achieved by generating reconstructed block data through decoding a current block from an encoded bitstream, using refined motion vectors obtained with a dynamic range adjusted autocorrelation matrix, and applying bilateral matching with warped refinement models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing video coding techniques are used, then bandwidth utilization is reduced, but manufacturing precision (encoding/decoding accuracy) deteriorates due to limitations in handling dynamic range and high-dimensional inverse autocorrelation
Solution Approach 1:
The patent applies parameter changes by using a dynamic range adjusted autocorrelation matrix that modifies the autocorrelation parameters based on the specific characteristics of the video data. This allows the encoding system to adaptively change parameters to maintain high accuracy while working within limited bandwidth constraints, resolving the contradiction between bandwidth efficiency and encoding precision.
Solution Approach 2:
The patent implements dynamics through adaptive optical flow refinement that dynamically adjusts the refinement process based on the complexity of motion in different video regions. By making the encoding process dynamic rather than static, the system can achieve high manufacturing precision only where necessary, thereby reducing overall bandwidth utilization while maintaining accuracy where it matters most.
2Manufacturing precision
If high-resolution video data is transmitted, then manufacturing precision (video quality) is improved, but loss of substance (data volume) increases leading to bandwidth constraints
Solution Approach 1:
The patent extracts and transmits only the essential motion information and residual data after applying bilateral matching and optical flow refinement. By separating and transmitting only the critical components rather than the entire high-resolution data, the system maintains video quality while significantly reducing the data volume that needs to be transmitted over bandwidth-constrained channels.
Solution Approach 2:
The patent segments the video data into different components (motion vectors, residuals, reference frame data) and processes each segment with appropriate refinement techniques. This segmentation allows selective application of compression techniques to different data types, maintaining overall video quality while optimizing the total data volume for transmission within bandwidth limits.
3Measurement precision
If dynamic range adjusted autocorrelation matrix is used, then measurement precision (motion vector accuracy) is improved, but device complexity increases
Solution Approach 1:
The patent applies partial action by using the dynamic range adjusted autocorrelation matrix selectively only for obtaining refined motion vectors, rather than applying it to all processing stages. This partial application achieves the measurement precision improvement where it is most needed (in motion vector accuracy) while avoiding the computational complexity overhead in other processing areas, thus resolving the contradiction between precision and complexity.
Data Source
AI summary
Coding including dynamic range handling of high dimensional inverse autocorrelation in optical flow refinement includes obtaining a refinement model from available warped refinement models, wherein the available warped refinement models include a four-parameter scaling refinement model, a three-parameter scaling refinement model, and a four-parameter rotational refinement model, obtaining refined motion vectors using the warped refinement model and previously obtained reference frame data in the absence of data expressly indicating the refined motion vectors in the encoded bitstream, wherein obtaining the refined motion vectors includes using a dynamic range adjusted autocorrelation matrix, generating refined prediction block data using the refined motion vectors, generating reconstructed block data using the refined prediction block data, including the reconstructed block data in reconstructed frame data for the current frame, and outputting the reconstructed frame data.