Tensor Product B-Spline Image Compression
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Solution Overview
Problem
Existing image compression methods struggle to balance file size reduction with maintaining image quality, particularly in high dynamic range (HDR) images, and often require iterative computations that are computationally intensive.
Innovation Solution
The use of tensor product B-spline (TPB) representations to model images as continuous functions, applying spatial reshaping and patch-wise TPB models to redistribute content complexity uniformly, followed by quantization and arithmetic coding of coefficients for metadata transmission, reduces the need for iterative computations and enhances compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional image compression methods are used, then file size reduction is achieved, but image quality deteriorates significantly, especially in HDR images
Solution Approach 1:
The image is divided into multiple patches, and each patch is processed independently with its own TPB model. This segmentation allows tailored compression for different regions, preserving quality in complex areas while compressing uniform areas more aggressively.
Solution Approach 2:
The patent changes the representation parameters from conventional pixel-based models to tensor product B-spline models with spatial reshaping. This parameter transformation enables better compression by modeling images as continuous functions with adjustable basis functions.
2Productivity
If iterative computations are used to improve compression, then compression performance improves, but computational complexity and processing time increase
Solution Approach 1:
The patent replaces iterative optimization mechanisms with direct TPB model fitting. Instead of iteratively adjusting parameters to minimize error, the method directly computes TPB coefficients that represent the image as continuous functions, eliminating the need for iterative computations while maintaining compression effectiveness.
3Manufacturing precision
If higher bit rates are used to maintain image quality, then compression efficiency improves, but memory requirements and processing power increase
Solution Approach 1:
The patent transforms the image representation into TPB models with spatial reshaping, which changes the fundamental parameters of image representation. This transformation allows achieving high image quality at lower bit rates by efficiently capturing image structure through continuous function models rather than requiring high bit rates.
Data Source
AI summary
Methods and apparatus for image compression/decompression using tensor product B-spline (TPB) representations. According to an example embodiment, an image-compression method includes generating a plurality of TPB models representing an image, including a TPB model for approximating a spatial reshaping function and a plurality of patch-wise TPB models for estimating the image signal. In some examples, the plurality of patch-wise TPB models includes a plurality of luma-channel TPB models and a plurality of chroma-channel TPB models. The spatial reshaping function is configured to shift a non-uniform distribution of local content complexity in the image toward being more uniform. The method also includes generating a metadata stream carrying metadata representing sets of coefficients of various TPB models. At least a portion of the metadata is generated by quantizing the corresponding coefficients to a smaller number of bits and applying arithmetic coding to the quantized coefficients.