Orthogonal Residual Block Projection for Image Compression
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Solution Overview
Problem
Current block-based coding techniques for image data compression, such as the H.264 standard, face inefficiencies in energy management and information loss during quantization due to non-orthogonal residual data blocks, which affect compression efficiency and quality.
Innovation Solution
The technique involves deriving a residual block by scaling a reference block with a projection factor calculated based on the current block, ensuring orthogonality, and then entropy encoding the transform coefficients, with the projection factor also being encoded and decoded to reconstruct the original block accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block-based coding techniques are used for image compression, then compression efficiency is improved, but information loss during quantization increases due to non-orthogonal residual data blocks
Solution Approach 1:
The patent applies parameter changes by introducing a projection factor that transforms the residual data blocks to achieve orthogonality. This mathematical transformation modifies the parameters of the residual blocks, ensuring they are orthogonal to each other, which minimizes information loss during quantization while preserving compression efficiency.
Solution Approach 2:
The patent replaces the traditional mechanical block-based coding approach with a mathematical transformation system. Instead of directly quantizing non-orthogonal residual blocks, the system substitutes a projection operation that computes orthogonal residual blocks through matrix multiplication with a projection factor, thereby reducing quantization error.
2Device complexity
If residual data blocks are used without orthogonality enforcement, then encoding process is simpler, but energy in residual blocks increases leading to higher quantization error
Solution Approach 1:
The patent modifies the parameters of residual data blocks by applying a projection transformation. The projection factor changes the orientation and magnitude of residual blocks, ensuring they achieve orthogonality. This parameter transformation reduces the energy content of residual blocks, thereby minimizing quantization error while maintaining manageable encoding complexity.
3Measurement precision
If projection factor is calculated and applied to ensure orthogonality, then quantization error is reduced, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating the projection factor based on the relationship between reference and current blocks. This projection factor is computed in advance before the actual quantization process, allowing the system to achieve high quantization accuracy without repeatedly performing complex calculations during encoding and decoding operations.
Solution Approach 2:
The projection factor serves as an intermediary element that mediates between the reference blocks and current blocks. Instead of directly comparing and quantizing complex block relationships, the system uses the projection factor as an intermediate mathematical representation, simplifying the overall computational process while maintaining high precision.
Data Source
AI summary
Methods, systems, and computer programs for encoding and decoding image are described. In some aspects, an input data block and a prediction data block are accessed. A projection factor is generated based on a projection of the input data block onto the prediction data block. A scaled prediction data block is generated by multiplying the projection factor by the prediction data block. A residual data block is generated based on a difference between the input data block and the scaled prediction data block. In some aspects, a prediction data block, a residual data block, and a projection factor associated with the residual data block are accessed. A scaled prediction data block is generated by multiplying the projection factor by the prediction data block. An output data block is generated by summing the residual data block and the scaled prediction data block.