Abstract: Methods, systems, articles of manufacture, and apparatus to enhance image depth confidence maps are disclosed. An apparatus includes a gradient analyzer to: generate an image gradient based on a digital image of a scene; and generate a depth gradient based on a depth map associated with the digital image. The depth map is to define depth values corresponding to pixels in the digital image. The apparatus further includes an enhanced confidence map analyzer to determine an enhanced confidence map for the depth map based on the image gradient, the depth gradient, and an initial confidence map for the depth map.

Abstract: Techniques are provided for unsupervised training of a neural network to perform compression of a high dynamic range (HDR) image. A methodology implementing the techniques according to an embodiment includes performing global tone mapping on an HDR training image to generate a low dynamic range (LDR) training image. The method also includes applying the neural network to the HDR training image and the LDR training image to generate a delta image representing image detail lost in the global tone mapping operation. The method further includes summing the delta image with the LDR training image to generate an output training image, and generating a loss function calculated from a weighted sum of a contrast loss and a compression loss. The contrast loss is based on the output training image and the HDR training image, and the compression loss is based on the output training image and the LDR training image.

Abstract: Techniques related to detecting texture content in an input image are discussed. Such techniques include accumulating pixel-wise directional gradient counts for positive and negative gradients for a first direction and positive and negative gradients for a second direction for pixels in a pixel window. A texture value for the target pixel of the window is determined based on a sum of a minimum of the positive and negative gradients for a first direction and a minimum of the positive and negative gradients for a second direction.

Abstract: Methods, systems, articles of manufacture, and apparatus to enhance image depth confidence maps are disclosed. An apparatus includes a gradient analyzer to: generate an image gradient based on a digital image of a scene; and generate a depth gradient based on a depth map associated with the digital image. The depth map is to define depth values corresponding to pixels in the digital image. The apparatus further includes an enhanced confidence map analyzer to determine an enhanced confidence map for the depth map based on the image gradient, the depth gradient, and an initial confidence map for the depth map.

Abstract: Techniques are provided for unsupervised training of a neural network to perform compression of a high dynamic range (HDR) image. A methodology implementing the techniques according to an embodiment includes performing global tone mapping on an HDR training image to generate a low dynamic range (LDR) training image. The method also includes applying the neural network to the HDR training image and the LDR training image to generate a delta image representing image detail lost in the global tone mapping operation. The method further includes summing the delta image with the LDR training image to generate an output training image, and generating a loss function calculated from a weighted sum of a contrast loss and a compression loss. The contrast loss is based on the output training image and the HDR training image, and the compression loss is based on the output training image and the LDR training image.

Abstract: Techniques related to detecting texture content in an input image are discussed. Such techniques include accumulating pixel-wise directional gradient counts for positive and negative gradients for a first direction and positive and negative gradients for a second direction for pixels in a pixel window. A texture value for the target pixel of the window is determined based on a sum of a minimum of the positive and negative gradients for a first direction and a minimum of the positive and negative gradients for a second direction.

Abstract: An apparatus for a near lossless compression scheme is described herein. The apparatus includes an image processing pipeline and a compressor. The image processing pipeline is to apply a plurality of processing stages to high dynamic range images to output a plurality of intermediate processed images, wherein each image comprises a plurality of pixel components. The compressor is to generate compression tiles of the plurality of intermediate processed images, wherein each output of the plurality of processing stages is compressed independently of the other processing stage.

Abstract: An apparatus for a near lossless compression scheme is described herein. The apparatus includes an image processing pipeline and a compressor. The image processing pipeline is to apply a plurality of processing stages to high dynamic range images to output a plurality of intermediate processed images, wherein each image comprises a plurality of pixel components. The compressor is to generate compression tiles of the plurality of intermediate processed images, wherein each output of the plurality of processing stages is compressed independently of the other processing stage.