Abstract: A method and apparatus are provided. The method includes generating a dataset for real-world super resolution (SR), training a first generative adversarial network (GAN), training a second GAN, and fusing an output of the first GAN and an output of the second GAN.

Abstract: A method and apparatus are provided. The method includes generating a dataset for real-world super resolution (SR), training a first generative adversarial network (GAN), training a second GAN, and fusing an output of the first GAN and an output of the second GAN.

Abstract: Input minimal noise levels of are computed over input codeword bins based on image content in input images of an input bit depth. The minimal noise levels are adjusted to generate approximated minimal noise levels of a higher bit depth. The approximated minimal noise levels are used to generate per-bin bit depths over the input codeword bins. The input codeword bins are classified into first codeword bins that have relatively high risks of banding artifacts and some other input codeword bins that have relatively low or zero risks of banding artifacts based on the per-bin bit depths. Portions of bit depths from the other input codeword bins are moved to the first input codeword bins to generate modified per-bin bit depths. A forward reshaping function constructed from the modified per-bin bit depths is used to reshape the input images into reshaped images used to generate output images.

Abstract: An image processing device receives one or more forward reshaped images that are generated by an image forward reshaping device from one or more wide dynamic range images based on a forward reshaping function. The forward reshaping function relates to a backward reshaping function. The image processing device performs one or more image transform operations on the one or more forward reshaped images to generate one or more processed forward reshaped images without performing backward reshaping operations on the one or more reshaped images or the one or more processed forward reshaped images based on the backward reshaping function. The one or more processed forward reshaped images are sent to a second image processing device.

Abstract: Input minimal noise levels of are computed over input codeword bins based on image content in input images of an input bit depth. The minimal noise levels are adjusted to generate approximated minimal noise levels of a higher bit depth. The approximated minimal noise levels are used to generate per-bin bit depths over the input codeword bins. The input codeword bins are classified into first codeword bins that have relatively high risks of banding artifacts and some other input codeword bins that have relatively low or zero risks of banding artifacts based on the per-bin bit depths. Portions of bit depths from the other input codeword bins are moved to the first input codeword bins to generate modified per-bin bit depths. A forward reshaping function constructed from the modified per-bin bit depths is used to reshape the input images into reshaped images used to generate output images.

Abstract: A processor for signal reshaping receives an input image with an input bit depth. Block-based standard deviations are computed. The input codewords are divided into codeword bins and each bin is assigned a standard deviation value. For each bin, a standard deviation to bit-depth function is applied to the bin values to generate minimal bit depth values for each codeword bin. An output codeword mapping function is generated based on the input bit depth, a target bit depth, and the minimal bit depth values. The codeword mapping function is applied to the input image to generate an output image in the target bit depth.

Abstract: A processor for signal reshaping receives an input image with an input bit depth. Block-based standard deviations are computed. The input codewords are divided into codeword bins and each bin is assigned a standard deviation value. For each bin, a standard deviation to bit-depth function is applied to the bin values to generate minimal bit depth values for each codeword bin. An output codeword mapping function is generated based on the input bit depth, a target bit depth, and the minimal bit depth values. The codeword mapping function is applied to the input image to generate an output image in the target bit depth.

Abstract: An image processing device receives one or more forward reshaped images that are generated by an image forward reshaping device from one or more wide dynamic range images based on a forward reshaping function. The forward reshaping function relates to a backward reshaping function. The image processing device performs one or more image transform operations on the one or more forward reshaped images to generate one or more processed forward reshaped images without performing backward reshaping operations on the one or more reshaped images or the one or more processed forward reshaped images based on the backward reshaping function. The one or more processed forward reshaped images are sent to a second image processing device.