Modified Inverse Transform Scaling for Video Prediction
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Solution Overview
Problem
Current video compression methods face challenges in efficiently processing next-generation video content with high spatial resolution and frame rate, leading to increased memory and processing demands, and suffer from weakened correlation due to quantization noise and computational complexity.
Innovation Solution
A method is introduced that applies a different reconstruction base to each transform coefficient by using a modified inverse-transform with a unique scaling matrix for each pixel location in a neighboring block, enhancing the reference pixel for prediction and reducing quantization noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple prediction methods are used, then computational complexity is reduced, but correlation between original data and prediction data is weakened
Solution Approach 1:
The patent applies different scaling matrices to different pixel locations within the same block, making the reconstruction process locally adaptive rather than uniformly simple. This local differentiation maintains correlation by tailoring reconstruction to local characteristics while keeping overall computational complexity manageable through the structured approach of using predefined scaling matrices.
2Device complexity
If a single reconstruction base is applied to all transform coefficients, then processing is simplified, but prediction accuracy deteriorates due to quantization noise
Solution Approach 1:
The patent changes the reconstruction parameters by applying different scaling matrices to different transform coefficients based on their position and characteristics. This parameter differentiation allows the system to adapt to local variations in the data, improving prediction accuracy by reducing quantization noise effects while maintaining reasonable processing complexity through the systematic application of scaling rules.
3Manufacturing precision
If high spatial resolution and high frame rate are targeted, then video quality is improved, but memory storage and processing power requirements increase drastically
Solution Approach 1:
The patent segments the reconstruction process by applying different scaling matrices to different regions and coefficients within video blocks. This segmentation allows for more efficient processing of high-resolution video by treating different parts of the image with appropriate levels of detail, reducing the overall computational burden and memory requirements while maintaining high video quality through localized optimization.
Data Source
AI summary
Disclosed is a method allowing enhanced prediction of a video signal, the method comprising the steps of: entropy-decoding a neighboring block adjacent to a target block; inverse-quantizing the entropy-decoded neighboring block; acquiring the modified neighboring block by carrying out a modified inverse transform on an inverse-quantized transform coefficient vector of the neighboring block; and generating a prediction block for the target block based on the modified neighboring block, wherein the modified inverse transform applies a different scaling matrix for each pixel location reconstructed for the inverse-quantized transform coefficient vector of the neighboring block.