8 February 2015 Optimizing color fidelity for display devices using vectorized interpolation steered locally by perceptual error quantities
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Abstract
High-end PC monitors and TVs continue to increase their native display resolution to 4k by 2k and beyond. Subsequently, uncompressed pixel amplitude processing becomes costly not only when transmitting over cable or wireless communication channels, but also when processing with array processor architectures. We recently presented a block-based memory compression architecture for text, graphics, and video which we named parametric functional compression (PFC) enabling multi-dimensional error minimization with context sensitive control of visually noticeable artifacts. The underlying architecture was limited to small block sizes of 4x4 pixels. Although well suitable for random access, its overall compression ratio ranges between 1.5 and 2.0. To increase compression ratio as well as image quality, we propose a new hybrid approach. Within an extended block size we apply two complementary methods using a set of vectors with orientation and curvature attributes across a 3x3 kernel of pixel positions. The first method searches for linear interpolation candidate pixels that result in very low interpolation errors using vectorized linear interpolation (VLI). The second method calculates the local probability of orientation and curvature (POC) to predict and minimize PFC coding errors. Detailed performance estimation in comparison with the prior algorithm highlights the effectiveness of our new approach, identifies its current limitations with regard to high quality color rendering with lower number of bits per pixel, and illustrates remaining visual artifacts.
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Marina Nicolas, Fritz Lebowsky, "Optimizing color fidelity for display devices using vectorized interpolation steered locally by perceptual error quantities", Proc. SPIE 9395, Color Imaging XX: Displaying, Processing, Hardcopy, and Applications, 939502 (8 February 2015); doi: 10.1117/12.2085108; https://doi.org/10.1117/12.2085108
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