Adaptive Media Compression via Residual Sub-Error Segmentation
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Solution Overview
Problem
Traditional video compression methods apply the same transformation and quantization functions to entire blocks of pixels, lacking flexibility to adjust processing parameters based on the relative importance of different aspects of the media object, which can lead to increased distortion in critical image features like edges.
Innovation Solution
The system decomposes residual errors into sub-errors based on image portions, applying different transformation and quantization functions to each sub-error, allowing for more precise processing of important features like edges while using less efficient methods for less critical areas, and signals these parameters in the bit stream for accurate decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the same transformation and quantization functions are applied to entire blocks of pixels, then the processing is simple and uniform, but the image quality deteriorates due to increased distortion in critical features like edges
Solution Approach 1:
The residual error signal is decomposed into multiple sub-error signals corresponding to different image portions (e.g., edge regions and non-edge regions). Each sub-error signal is then processed independently with appropriate transformation and quantization functions, allowing critical features to be preserved while maintaining compression efficiency.
Solution Approach 2:
Different transformation and quantization functions are applied to different sub-error signals based on their local characteristics. For example, edge-related sub-errors may use transformations that preserve sharp transitions, while non-edge sub-errors may use more aggressive quantization. This local adaptation improves overall image quality without uniformly increasing complexity.
2Manufacturing precision
If different transformation and quantization functions are applied to different sub-errors, then image quality improves by preserving critical features, but the device complexity increases
Solution Approach 1:
The residual error decomposition into sub-error signals creates a structured framework where different processing can be applied. The encoder determines which sub-errors to decompose and which processing functions to apply, while the decoder reconstructs the image by combining the processed sub-errors. This segmentation allows quality improvement without requiring completely different encoder/decoder architectures.
Solution Approach 2:
The system selectively applies different transformation and quantization parameters to different sub-error signals based on their characteristics. This parameter adaptation is controlled through signaling in the bitstream, allowing the system to optimize image quality by changing processing parameters rather than changing the fundamental processing architecture, thus managing complexity.
3Productivity
If aggressive compression is applied to entire blocks, then compression efficiency increases, but distortion in important image aspects increases
Solution Approach 1:
By segmenting the residual error into sub-error signals representing different image portions, the system can apply different compression strengths to different regions. Critical features like edges can be preserved with less aggressive compression, while non-critical areas can undergo more aggressive compression, optimizing the overall compression efficiency without uniformly sacrificing quality.
Solution Approach 2:
The system applies local quality control by selecting different transformation and quantization functions for different sub-error signals. Regions containing important image features use processing functions that minimize distortion, while less important regions use more aggressive compression. This local adaptation allows the system to achieve high compression efficiency while preserving critical image quality.
Data Source
AI summary
A system comprises an encoder configured to compress media objects using a compression loop that includes a residual decomposition component that decomposes a residual signal for a block of the media object being compressed into multiple sub-error signals. The encoder is further configured to enable different transformation and/or quantization processes to be specified to be applied to different ones of the sub-errors. A corresponding decoder is configured to apply inverse transformation/quantization processing to the sub-error signals, based on the transformation/quantization processes that were applied at the encoder. The decoder then re-creates a residual signal from the processed sub-error signals and uses the re-created residual signal to correct predicted values at the decoder.