Inter-Component Prediction for Multi-Component Picture Coding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-component picture coding techniques face inefficiencies in bit rate management and correlation removal across different color components, particularly in hybrid video compression schemes, leading to sub-optimal results due to fixed color space transformations and increased complexity in image and video processing.
Innovation Solution
A decoder and encoder system that employs inter-component prediction by reconstructing color components using a spatially corresponding portion of the first component signal and a correction signal derived from the data stream, allowing for adaptive weighting and domain switching between spatial and spectral domains to optimize coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed color space transformations are applied to remove correlation between color components, then compression efficiency is improved, but information loss occurs and the transformation becomes sub-optimal for higher bit rates and non-natural signals
Solution Approach 1:
The patent applies dynamics by replacing fixed color space transformations with adaptive transformations that can change based on signal characteristics. The system dynamically selects transformation parameters (e.g., transformation matrices) according to the statistical properties of the input signal, allowing optimal correlation removal while preserving information. This adaptive approach enables the system to handle both natural and non-natural signals effectively, resolving the contradiction between compression efficiency and information preservation.
Solution Approach 2:
The patent changes parameters by introducing variable transformation parameters instead of fixed ones. The transformation parameters (such as transformation matrices or weighting factors) are adjusted based on signal characteristics like bit rate, signal type, and correlation properties. This parameter adaptation allows the system to optimize correlation removal for different应用场景 while maintaining information integrity, thereby resolving the sub-optimality of fixed transformations at higher bit rates.
2Ease of manufacture
If fixed color space transformations are applied globally, then processing is simplified, but correlation is not completely removed from different components locally or globally
Solution Approach 1:
The patent applies segmentation by dividing the global transformation into local or regional transformations. Instead of applying a single fixed transformation matrix to the entire image, the system segments the image into multiple regions and applies different transformation parameters to each region based on local correlation characteristics. This segmented approach maintains processing simplicity while significantly improving correlation removal effectiveness, as each region can be optimized independently for its specific signal properties.
Solution Approach 2:
The patent implements local quality by making transformation parameters spatially varying rather than uniform. Different regions of the image can have different transformation parameters adapted to their local correlation structures. This allows the system to maintain simple processing architecture while achieving reliable correlation removal, as the transformation is tailored to local signal characteristics rather than applying a one-size-fits-all approach.
3Quantity of substance
If color space transformation is applied to reduce correlation between color components, then less information has to be transmitted, but the transformation introduces additional processing complexity
Solution Approach 1:
The patent applies partial action by implementing transformation only where and when necessary. Instead of always applying a complex adaptive transformation, the system evaluates the signal characteristics and applies transformation only when correlation removal benefits exceed the processing complexity cost. For signals with low correlation or at lower bit rates, the system may skip transformation or use simpler methods. This selective application reduces overall processing complexity while maintaining information compression benefits where most valuable.
Data Source
AI summary
Reconstructing a second component signal relating to a second component of a multi-component picture from a spatially corresponding portion of a reconstructed first component signal and a correction signal derived from data stream for the second component promises increased coding efficiency over a broader range of multi-component picture content. By including the spatially corresponding portion of the reconstructed first component signal into the reconstruction of the second component signal, any remaining inter-component redundancies/correlations present such as still present despite a possibly a priori performed component space transformation, or present because of having been introduced by such a priori performed component space information, for example, may readily be removed by way of the inter-component redundancy/correlation reduction of the second component signal.