Circular-Shift Transform Coding for Block Boundary Quantization Errors
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Solution Overview
Problem
Existing image and video compression schemes face challenges in efficiently reducing quantization errors at block boundaries, leading to non-uniform error distribution and increased complexity due to the use of multiple transforms like Discrete Cosine Transform and Karhunen-Loève Transform, which increase bandwidth utilization and computational complexity.
Innovation Solution
Implementing circular-shift transformation to adapt pixel data for a defined transform, such as Discrete Cosine Transform, by identifying optimal circular-shift offsets to move boundary pixels to interior positions, reducing distortion and simplifying quantization complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple transforms like DCT and KLT are used to reduce quantization errors, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The patent extracts and addresses only the most critical issue (boundary pixel quantization errors) rather than applying multiple complex transforms. By focusing on a single problem area and applying a targeted circular-shift solution, the patent reduces overall system complexity while still improving quantization error distribution.
Solution Approach 2:
The patent changes the parameter of pixel positioning by applying circular-shift offsets to move boundary pixels to interior positions before transformation. This parameter change in pixel arrangement allows for improved quantization error distribution using a single, simpler transform rather than multiple complex transforms.
2Manufacturing precision
If multiple transforms are used to improve coding performance, then manufacturing precision improves, but ease of operation worsens
Solution Approach 1:
The patent applies preliminary circular-shift operations to reposition boundary pixels before the main transformation process. This preliminary action simplifies the subsequent coding operations by pre-processing the pixel data in a way that reduces quantization errors, making the overall coding process easier to implement.
3Manufacturing precision
If circular-shift transformation is applied to move boundary pixels to interior positions, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The patent applies circular-shift operations selectively to only those pixels that benefit most from repositioning (boundary pixels prone to quantization errors), rather than uniformly processing all pixels. This localized approach improves distortion reduction while minimizing unnecessary computational time expenditure on pixels that don't require repositioning.
Data Source
AI summary
Image coding using circular-shift transformation includes generating a reconstructed image by obtaining a circular-shift indicator indicating that circular-shift transformation is enabled for a current block by decoding the circular-shift indicator from an encoded bitstream, obtaining quantized transform coefficients for the current block by entropy decoding the quantized transform coefficients from the encoded bitstream, obtaining circular-shift offsets for the current block by decoding the circular-shift offsets from the encoded bitstream, obtaining dequantized transform coefficients for the current block by dequantizing the quantized transform coefficients, obtaining reconstruction circular-shifted residual values for the current block by inverse transforming the dequantized transform coefficients, obtaining reconstruction residual values for the current block by inverse circular shifting the reconstruction circular-shifted residual values, generating prediction values for the current block, obtaining reconstructed pixels for the current block by combining the reconstruction residual values and the prediction values, and including the reconstructed pixel in the reconstructed image.