A device includes an encoder, decoder, codec or combination thereof and inline hardware conversion units that are operative to convert stored image data into one of: an HDR/WCG format and an SDR/SCG format during the conversion process. Each of the inline hardware conversion units is operative to perform the conversion process independent of another read operation with the memory that stores the image data to be converted. In one example, an encoding unit is operative to perform a write operation with a memory to store the converted image data after completing the conversion process. In another example, a decoding unit is operative to perform a read operation with the memory to retrieve the image data from the memory before initiating the conversion process. In another example, an encoder/decoder unit is operative to perform at least one of: the read operation and the write operation.

A method includes obtaining, using at least one image sensor of an electronic device, a first image frame and multiple second image frames of a scene. Each of the second image frames has an exposure time different from an exposure time of the first image frame. The method also includes encoding, using at least one processor, each of the first image frame and the second image frames using a convolutional neural network to generate a corresponding feature map. The method further includes aligning, using the at least one processor, encoded features of the feature map corresponding to the first image frame with encoded features of the feature maps corresponding to the second image frames.

In some aspects, a device may receive, from a pixel array of a camera, a first image. The device may configure, based at least in part on the first image, a setting of a filter. The filter may be included within a filter array that is arranged within the camera in association with the pixel array. The device may cause the pixel array to capture a second image. Numerous other aspects are described.

In various examples, apparatuses, systems, and techniques to perform offline image signal processing of source image data to generate target image data. In at least one embodiment, data collection using exposure and calibration setting of an image sensor is performed to generate source image data, which is then processed by using offline image signal processing to generate target data.

An image processing apparatus including a determining unit configured to determine, based on at least one of a first conversion characteristic or a second conversion characteristic, a conversion characteristic of each of a plurality of regions in a first image having a first dynamic range, and a conversion unit configured to convert an image of each of the plurality of regions into a second image having a second dynamic range less than the first dynamic range by using the conversion characteristic determined by the determining unit. In relation to input luminance and output luminance, the first conversion characteristic has a higher characteristic of maintaining tone than the second conversion characteristic, and has a lower characteristic of maintaining contrast than the second conversion characteristic.

A method for transforming high dynamic range (HDR) video data into standard dynamic range (SDR) video data and encoding the SDR video data so that the HDR video data may be recovered at the decoder includes generating a tone map describing a transformation applied to the HDR video data to generate the SDR video data. The generated tone map describes the transformation as the multiplication of each HDR pixel in the HDR video data by a scalar to generate the SDR video data. The tone map is then modeled as a reshaping transfer function and the HDR video data is processed by the reshaping transfer function to generate the SDR video data. The reshaping transfer function is then inverted and described in a self-referential metadata structure. The SDR video data is then encoded including the metadata structure defining the inverse reshaping transfer function.

Described examples relate to an apparatus comprising a memory for storing image frames and at least one processor. The at least one processor may be configured to receive a plurality of image frames from an image capture device and downsize each of the plurality image frames to generate a plurality of versions of each image frame at a plurality of different sizes. The at least one processor may also be configured to determine alignment information for a first version of a first image frame. The alignment information may include a first alignment vector for identifying image data in a first version of a second image frame that corresponds to image data in the first version of the first image frame. Further, the at least one processor may be configured to determine a first initial alignment vector for identifying image data in a first version of a third image frame based on at least the first alignment vector.

Chroma correction of inverse gamut mapping (IGM) for standard dynamic range (SDR) to high dynamic range (HDR) image conversion includes: converting R,G,B color components in the RGB color format of a pixel of an image to an intensity component (I) and chroma components (Ct and Cp) of an ICtCp color format, wherein the R,G,B color components represent red, green, and blue colors; applying an intensity transformation operation on the intensity component (I) of the pixel; executing a chroma correction operation on the transformed intensity component (I) and the chroma components (Ct and Cp) of the pixel; and converting the intensity component (I) and the chroma components (Ct and Cp) of the pixel back to the RGB color format.

Embodiments are directed towards video enhancement in accordance with time constraint. An example method includes determining a time constraint for transforming low dynamic range (LDR) video content to high dynamic range (HDR) video content, processing the LDR video content to generate instructions for transforming to HDR video content in accordance with the time constraint, rendering the HDR video content based on executing the generated instructions; and producing metadata including the generated instructions for sharing.

Embodiments are directed towards metadata service for video enhancement. An example method includes receiving a request for metadata applicable to instruct a display device to transform low dynamic range (LDR) video content to high dynamic range (HDR) video content, determining availability of candidate metadata, and responsive to determining that no candidate metadata is available, providing options for generating the metadata applicable to instruct the display device. The example method can also include causing generation of the metadata based at least one of the options, and providing the generated metadata to the display device.