Spatially Extended Reference Pictures for Video Coding Prediction
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Solution Overview
Problem
Existing video coding technologies face challenges in efficiently utilizing spatial extrapolation as a predictor to enhance compression efficiency and improve picture quality, particularly in scenarios involving extended field-of-view (FOV) pictures.
Innovation Solution
The method involves forming an extended FOV picture, deriving a prediction signal using this picture, and applying it to encode or decode parts of a current source picture, leveraging global motion to spatially extend core pictures and derive prediction signals for improved encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If spatial extrapolation is used to extend reference pictures for prediction, then compression efficiency and picture quality are improved, but the complexity of the encoding and decoding process increases
Solution Approach 1:
The patent applies preliminary action by performing spatial extrapolation on reference pictures before they are used for prediction. The encoder pre-extends the reference pictures using extrapolation filters to create extended reference pictures, which are then stored and reused for predicting multiple current pictures. This preliminary preparation reduces the need for repeated complex operations during actual encoding, thereby improving compression efficiency while managing complexity.
Solution Approach 2:
The patent uses copying by creating extended reference pictures that are generated once and then copied/reused for predicting multiple current pictures. The extended reference pictures serve as reusable prediction sources, eliminating the need to perform spatial extrapolation repeatedly for each current picture, thus improving productivity while controlling device complexity.
2Measurement precision
If spatial extrapolation is applied to extend core pictures, then the prediction accuracy for current pictures is improved, but the computational resources and processing time required increase
Solution Approach 1:
The patent performs spatial extrapolation as a preliminary action to extend core reference pictures before they are needed for prediction. By pre-computing the extended reference pictures and storing them, the system avoids repeating the computationally intensive extrapolation process for each current picture, thereby maintaining high prediction accuracy while reducing processing time and computational resource consumption during actual encoding/decoding operations.
3Manufacturing precision
If extended FOV pictures are formed and used for prediction, then the quality of reconstructed pictures is improved, but the memory requirements and data processing load increase
Solution Approach 1:
The patent applies local quality by performing spatial extrapolation selectively on specific regions of reference pictures that are most beneficial for prediction. Rather than uniformly extending entire pictures, the method focuses computational resources on extending areas that will provide the most improvement in prediction accuracy, thereby achieving high picture quality while minimizing the increase in memory requirements and data processing load.
Data Source
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AI summary
A method comprising: forming an extended field-of-view (FOV) picture; deriving a prediction signal by using the extended-FOV picture; and using the prediction signal to encode at least a part of a current source picture to a current coded picture.