Block Adaptive Weighted Prediction for Local Illumination Variation
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Solution Overview
Problem
Existing video coding and decoding technologies struggle to effectively address local illumination variation, leading to inefficiencies in compression and transmission of video data.
Innovation Solution
Enhancing block adaptive weighted prediction (BAWP) by using block vectors to compensate for local illumination variation, involving methods and apparatus for decoding and encoding that include identifying block vectors, determining scaling factors, and reconstructing blocks based on predicted values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video coding techniques are used, then compression is achieved, but local illumination variation cannot be effectively addressed
Solution Approach 1:
The patent applies local quality by introducing block-specific scaling factors and offsets that are adapted to local illumination characteristics. Each block can have its own scaling factor (sf) and offset (off) parameters that are derived from block-level statistics, allowing the prediction to account for local lighting conditions rather than using a global model. This enables effective handling of local illumination variation while maintaining compression efficiency.
Solution Approach 2:
The patent changes parameters by introducing additional block-level parameters (scaling factor and offset) to the prediction model. These parameters are derived from block statistics and used to adjust the prediction values: predicted_block[i][j] = reference_block[i][j] + scaling_factor * (current_block[i][j] - reference block[i][j]) + offset. This parameter change enables the system to adapt to local illumination variations while maintaining computational efficiency.
2Reliability
If block adaptive weighted prediction is enhanced with block vectors, then local illumination variation is compensated, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing the scaling factors and offsets from block statistics before the actual prediction process. The scaling factor is derived from the standard deviation of block values, and the offset is derived from the mean values, both calculated in advance. This preliminary computation simplifies the actual prediction process during decoding, as only simple arithmetic operations with the pre-computed parameters are needed, thus limiting the increase in device complexity.
Solution Approach 2:
The patent replaces complex illumination modeling with a simplified linear transformation model. Instead of using complex neural networks or iterative optimization to model illumination effects, the system uses a straightforward linear equation with pre-computed scaling factors and offsets. This substitution maintains effective illumination compensation while significantly reducing computational complexity and making the system more practical for real-time video decoding.
Data Source
AI summary
This disclosure relates generally to video coding/decoding and particularly for enhancing block adaptive weighted prediction (BAWP) with a block vector. One method includes receiving a coded video bitstream; identifying, from the coded video bitstream, a block vector corresponding to a reference block associated with the current block of the current frame; determining a scaling factor based on a syntax explicitly signaled in the coded video bitstream; generating a predicted block based on the reference block according to a linear equation associated with the scaling factor; and reconstructing, by the device, the current block based on the predicted block.