This paper analytically formulates both the optimal quantization noise allocation ratio and the coding gain of the two-dimensional morphological Haar wavelet transform. The two-dimensional morphological Haar wavelet t...
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This paper analytically formulates both the optimal quantization noise allocation ratio and the coding gain of the two-dimensional morphological Haar wavelet transform. The two-dimensional morphological Haar wavelet transform has been proposed as a nonlinear wavelet transform. It has been anticipated for application to nonlinear transform coding. To utilize a transformation to transformcoding, both the optimal quantization noise allocation ratio and the coding gain of the transformation should be derived beforehand regardless of whether the transformation is linear or nonlinear. The derivation is crucial for progress of nonlineartransform image coding with nonlinear wavelet because the two-dimensional morphological Haar wavelet is the most basic nonlinear wavelet. We derive both the optimal quantization noise allocation ratio and the coding gain of the two-dimensional morphological Haar wavelet transform by introducing appropriate approximations to handle the cumbersome nonlinear operator included in the transformation. Numerical experiments confirmed the validity of formulations.
Given a standard image codec, we compress images that may have higher resolution and/or higher bit depth than allowed in the codec's specifications, by sandwiching the standard codec between a neural pre-processor...
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ISBN:
(纸本)9781665492577
Given a standard image codec, we compress images that may have higher resolution and/or higher bit depth than allowed in the codec's specifications, by sandwiching the standard codec between a neural pre-processor (before the standard encoder) and a neural post-processor (after the standard decoder). Using a differentiable proxy for the the standard codec, we design the neural pre- and post-processors to transport the high resolution (super-resolution, SR) or high bit depth (high dynamic range, HDR) images as lower resolution and lower bit depth images. The neural processors accomplish this with spatially coded modulation, which acts as watermarks to preserve the important image detail during compression. Experiments show that compared to conventional methods of transmitting high resolution or high bit depth through lower resolution or lower bit depth codecs, our sandwich architecture gains similar to 9 dB for SR images and similar to 3 dB for HDR images at the same rate over large test sets. We also observe significant gains in visual quality.
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