A display apparatus includes at least one memory and at least one processor which function as a processing unit, a display control unit, and a setting unit. The processing unit is configured to perform first image processing and second image processing. The display control unit is configured to perform control to perform a first display based on a result of the first image processing and a second display based on a result of the second image processing, together. The setting unit is configured to set upper-limit brightness of the second display. An Electro-Optical Transfer Function (EOTF) type of the first display and an EOTF type of the second display are EOTF types dealing with high dynamic range (HDR). The processing unit converts brightness of an image according to the upper-limit brightness and the EOTF type of the first display in the second image processing.

An image processing apparatus comprises a changing unit configured to change a display area of an image from a first display area to a second display area including at least a portion of the first display area, an acquiring unit configured to acquire a first value indicating luminance, in which brightness contrast is considered, in an image displayed in the first display area and a second value indicating luminance, in which brightness contrast is considered, in an image displayed in the second display area, and a correcting unit configured to correct luminance of the image displayed in the second display area based on the first value and the second value that are acquired by the acquiring unit.

This application provides a dynamic range mapping method and apparatus. The dynamic range mapping method includes: obtaining a display parameter of a terminal device; obtaining feature information of image data; obtaining a first parameter of a first tone mapping curve of the image data; when a preset condition is met, obtaining a second parameter of a second tone mapping curve based on the first parameter, the display parameter of the terminal device, and the feature information of the image data, where output luminance at a first point on the second tone mapping curve is not greater than input luminance at the first point on the second tone mapping curve; and performing dynamic range mapping on the image data based on the second parameter of the second tone mapping curve.

The present disclosure relates generally to the field of video processing, and more particularly, to high dynamic range (HDR) image processing. In particular, the present disclosure relates to a method for determining a parameter set for a tone mapping curve. The method comprises obtaining a plurality of parameter sets, wherein each parameter set defines the tone mapping curve, and wherein each parameter set is derived based on one of a plurality of HDR video frames. Further, the method comprises temporally filtering the plurality of parameter sets to obtain a temporally filtered parameter set.

The present disclosure relates generally to the field of video processing, and more particularly to high dynamic range (HDR) video and image processing. In particular, the present disclosure relates to determining one or more curve parameters of a tone mapping curve. For example, a device (encoder or decoder) may obtain a high dynamic range (HDR) video frame and metadata associated to the HDR video frame. The device further obtains a pair of anchor points, based on the HDR video frame and the metadata. The pair of anchor points comprises a first anchor point and a second anchor point of the tone mapping curve. Moreover, the device generates the one or more curve parameters of the tone mapping curve based on the pair of anchor points.

Certain aspects of the present disclosure provide an ophthalmic imaging device for enhancing images. The device comprises an image capture component configured to generate an image stream comprising a first frame preceding a second frame, both capturing a branch of veins in an eye. The device further comprises an image processor configured to calculate first order statistics for a plurality of blocks for the first frame and to interpolate first order statistics for the first frame based at least in part on the branch of veins. The image processor is also configured to generate a tone mapping function and calculate tone mapping values for individual pixels of the second frame based on the tone mapping function. The image processor is additionally configured to generate an enhanced frame based on the calculated tone mapping values for individual pixels of the second frame to the pixels of the second frame.

One embodiment provides a computer-implemented method that includes providing a machine learning network including a global inverse tone mapping (GITM) structure and a local inverse tone mapping (LITM) structure that utilize one or more non-linear basis functions with one or more coefficient functions. The one or more non-linear basis functions learn linearly to facilitate combination with at least one convolution layer for jointly learning the machine learning network. A weighted mask (WM) is provided for reducing one or more visual artifacts, including one or more quantization artifacts in a smooth region of an output of the machine learning network.

A conversion method for converting luminance of a video, including a luminance value in a first luminance range, to be displayed on a display apparatus includes: acquiring a first luminance signal indicating a code value obtained by quantization of the luminance value of the video; and converting the code value indicated by the acquired first luminance signal into a second luminance value determined based on a luminance range of the display apparatus, the second luminance value being compatible with a second luminance range with a maximum value smaller than a maximum value of the first luminance range and larger than 100 nit. This provides the conversion method capable of achieving further improvement.

Methods and systems are provided for preserving dynamic range in images. In some aspects, a process can include steps for receiving, at an autonomous vehicle system, image data from an image sensor, generating, by the autonomous vehicle system, a high dynamic range (HDR) output by performing HDR processing on the image data from the image sensor, generating, by the autonomous vehicle system, a least significant bit (LSB) output by performing LSB processing on the image data from the image sensor, and generating, by the autonomous vehicle system, an 8-bit output by performing a buffer process on the HDR output and the LSB output of the image sensor.

A method for image-processing and an electronic device are disclosed. The method includes: obtaining a RAW image packet comprising at least two RAW images, the at least two RAW images having different exposure durations; unpacking the RAW image packet and obtaining the at least two of RAW images; obtaining a High Dynamic Range (HDR) RAW image by performing an image synthesis operation on the at least two RAW images; and performing a previewing, photographing, or video-recording operation on the HDR RAW image.