113 results about "Standard dynamic range" patented technology

A high dynamic range editing system is configured to generate visualizations to aide digital image editing in both high dynamic ranges and standard dynamic ranges. In a first example, the visualization is generated as a histogram. In a second example, the visualization is generated to indicate high dynamic range capabilities. In a third example, the visualization is generated to indicate ranges of luminance values within a digital image. In a fourth example, the visualization is generated as a point curve that defines a mapping between detected luminance values from a digital image and output luminance values over both a standard dynamic range and a high dynamic range. In a fifth example, the visualization is generated as a preview to convert pixels from the digital image in a high dynamic range into a standard dynamic range.

A high dynamic range editing system is configured to generate visualizations to aide digital image editing in both high dynamic ranges and standard dynamic ranges. In a first example, the visualization is generated as a histogram. In a second example, the visualization is generated to indicate high dynamic range capabilities. In a third example, the visualization is generated to indicate ranges of luminance values within a digital image. In a fourth example, the visualization is generated as a point curve that defines a mapping between detected luminance values from a digital image and output luminance values over both a standard dynamic range and a high dynamic range. In a fifth example, the visualization is generated as a preview to convert pixels from the digital image in a high dynamic range into a standard dynamic range.

A method includes: obtaining original image information, where the original image information includes an original image and brightness level information; obtaining, based on the brightness level information included in the original image information and a brightness threshold, a thresholding segmentation result corresponding to the original image, where the thresholding segmentation result includes area class labels corresponding to pixels in the original image, the area class labels are used to indicate classes of areas corresponding to the pixels in the original image, the areas include a standard dynamic range area and an extended dynamic range area; and performing dynamic range extension on the extended dynamic range area in the original image based on the thresholding segmentation result corresponding to the original image, to obtain an extended dynamic range image corresponding to the original image.

Tensor-Product B-splines (TPB) have been shown to improve video quality when used to represent reshaping functions to map reshaped standard dynamic range content into high dynamic range (HDR) content; however, TPB prediction is computationally intensive and may not be supported by legacy devices. Methods and systems for backwards-compatible signaling of TPB-related metadata and a fast TPB prediction method are presented to overcome both of these limitations. Computation overhead for a TPB-based 3D look-up table is reduced by using temporary two-dimensional arrays. A remapping of the most significant bits of a legacy bit-depth parameter allows for backwards compatibility.

The embodiment of the invention provides an image loss determination method and a related device. The method comprises the following steps: generating a standard dynamic range (SDR) image according to an original high dynamic range (HDR) image; generating a reconstructed HDR image according to the original HDR image and the SDR image; and determining image loss of a plurality of areas in the reconstructed HDR image according to the original HDR image and the reconstructed HDR image. By adopting the embodiment of the invention, the reconstruction loss of the HDR image can be obtained, so that the visual fault in the reconstructed HDR image can be identified.

The invention provides an image processing method and device, and a storage medium. The method comprises the steps of obtaining a standard dynamic range (SDR) image; and inputting the SDR image into a color mapping model, performing color mapping and color gamut mapping on each pixel point in the SDR image by the color mapping model according to a color and color gamut mapping relation between the SDR image and a high dynamic range imaging HDR image to obtain color information and color gamut information of the HDR image, and generating the HDR image based on the color information and the color gamut information of the HDR image. The SDR image is converted into the HDR image, original data does not need to be collected according to the collection requirement of the HDR image, the requirement for hardware configuration of the electronic equipment is lowered, and the cost can be effectively saved. Meanwhile, the method for converting the SDR image into the HDR image based on the color mapping model obtained through training of a large amount of training data reduces the dependence on manual processing, and can save the labor cost while improving the efficiency.

A method comprising: obtaining standard dynamic range data; obtaining (601) information representative of an inverse tone mapping process adapted to generate high dynamic range data from the standard dynamic range data and inserting the information in metadata; providing video data representative of the standard dynamic range data (600) along with the metadata (602).

A high dynamic range editing system is configured to generate visualizations to aide digital image editing in both high dynamic ranges and standard dynamic ranges. In a first example, the visualization is generated as a histogram. In a second example, the visualization is generated to indicate high dynamic range capabilities. In a third example, the visualization is generated to indicate ranges of luminance values within a digital image. In a fourth example, the visualization is generated as a point curve that defines a mapping between detected luminance values from a digital image and output luminance values over both a standard dynamic range and a high dynamic range. In a fifth example, the visualization is generated as a preview to convert pixels from the digital image in a high dynamic range into a standard dynamic range.

A transmission method in the present disclosure includes; obtaining an image and image signal characteristics information indicating one of an opto-electrical transfer function (OETF) or an electro-optical transfer function (EOTF) as image signal characteristics of the image; and transmitting a signal including the image and the image signal characteristics information. According to the transmission method in the present disclosure, a receiving device that received a high dynamic range (HDR) image and a standard dynamic range (SDR) image transmitted through broadcasting or the like can display these images appropriately.

An electronic device may be provided with a display. The display may be configured to display standard dynamic range image content and high dynamic range image content at the same time. The control circuitry may dynamically adjust an amount of headroom for the high dynamic range image content to avoid washing out the standard dynamic range image content. For example, the control circuitry may reduce the amount of headroom available for high dynamic range image content with high average pixel luminance levels and low screen coverage. The headroom may be increased as the average pixel luminance levels decrease and / or as screen coverage increases. Different high dynamic range images that are displayed at the same time may be assigned different amounts of headroom (e.g., different peak brightness levels) based on average pixel luminance values, screen coverage, whether or not the content is within an active window on the display, etc.

A high dynamic range editing system is configured to generate visualizations to aide digital image editing in both high dynamic ranges and standard dynamic ranges. In a first example, the visualization is generated as a histogram. In a second example, the visualization is generated to indicate high dynamic range capabilities. In a third example, the visualization is generated to indicate ranges of luminance values within a digital image. In a fourth example, the visualization is generated as a point curve that defines a mapping between detected luminance values from a digital image and output luminance values over both a standard dynamic range and a high dynamic range. In a fifth example, the visualization is generated as a preview to convert pixels from the digital image in a high dynamic range into a standard dynamic range.

The invention relates to an HDR10 image layered coding and decoding method and device and a storage medium, and the method comprises the steps: receiving a YUV420 video data frame, carrying out the dynamic range segmentation of a pixel of each frame data, and dividing each pixel into a standard dynamic range part SDR and a high dynamic range part HDR; performing nonlinear quantization calculation on the pixel value brightness of the standard dynamic range part SDR and the pixel value brightness of the high dynamic range part HDR to obtain a brightness component # imgabs0 # of the base layer and a brightness component # imgabs1 # of the enhancement layer; based on the standard dynamic range part SDR and the high dynamic range part HDR, performing nonlinear quantization calculation on the chromaticity of each pixel value to obtain a chromaticity residual value # imgabs2 # and a chromaticity residual quantization value # imgabs3 #; and carrying out hierarchical coding and metadata encapsulation on pixels of the frame data to obtain an HDR10 code stream, and transmitting the HDR10 code stream. According to the invention, through a dynamic range segmentation algorithm, double-segment nonlinear quantization, hierarchical coding and dynamic metadata synchronization, high-efficiency coding transmission compatible with an HDR10 standard is realized.

An electronic device may be provided with a display. The display may be configured to display standard dynamic range image content and high dynamic range image content at the same time. The standard dynamic range image content may include one or more regions of locally boosted contrast in regions adjacent to high dynamic range image highlights. This prevents the appearance of grayish whites in standard dynamic range image content while allowing the high dynamic range highlights to take full advantage of the dynamic range of the display. The size and shape of the regions of locally increased contrast may be based on the size and shape of the high dynamic range highlights, the size of the display, the distance between the user and the display, and / or the percentage of the display that is dedicated to high dynamic range image content.

In a method to improve backwards compatibility when decoding high-dynamic range images coded in a wide color gamut (WCG) space which may not be compatible with legacy color spaces, hue and / or saturation values of images in an image database are computed for both a legacy color space (say, YCbCr-gamma) and a preferred WCG color space (say, IPT-PQ). Based on a cost function, a reshaped color space is computed so that the distance between the hue values in the legacy color space and rotated hue values in the preferred color space is minimized. HDR images are coded in the reshaped color space. Legacy devices can still decode standard dynamic range images assuming they are coded in the legacy color space, while updated devices can use color reshaping information to decode HDR images in the preferred color space at full dynamic range.

The present disclosure relates to systems and methods for transmitting standard dynamic range (SDR) content. The system and method may use a modified electro-optic transfer function (EOTF) curve to convert a non-linear color value of the SDR content to an optical output value of the modified SDR content. The system and method may encode the modified SDR content using 8 bits while preventing banding. The system and method may transmit the encoded data to a client device for presentation on a display.

The application relates to the technical field of digital film content production, distribution and screening, and discloses a high dynamic range content distribution display method and a video processing device. A standard dynamic range master is used as the only main asset, physical baseline information is generated, candidate residual information is extracted, semantic gating decision is performed, auxiliary data containing the physical baseline information and selectively containing conditional gain information is output, and a high dynamic range signal is reconstructed according to the corresponding mode selected according to the auxiliary data, so that the single master is adapted to different display capability screening terminals, and the problems of high cost and poor consistency of the existing multi-version distribution mode are solved. Meanwhile, the semantic gating mechanism is used to eliminate the AI illusion risk, the adaptive auxiliary data structure is used to reduce the transmission bandwidth, and the distribution efficiency, screening safety and content verifiability are considered.

The embodiments of the present application provide an image loss determination method and a related apparatus. The method comprises: generating a standard dynamic range (SDR) image on the basis of an original high dynamic range (HDR) image; generating a reconstructed HDR image on the basis of the original HDR image and the SDR image; and determining image losses of a plurality of regions in the reconstructed HDR image on the basis of the original HDR image and the reconstructed HDR image. Using the embodiments of the present application can obtain reconstruction losses of HDR images, thereby identifying visual contours in reconstructed HDR images.

This application discloses an image conversion method, device, electronic device, and computer-readable storage medium. The method includes: obtaining a standard dynamic range image and determining the RGB color values ​​of the standard dynamic range image; using a correction coefficient prediction model to predict the target correction coefficient corresponding to the standard dynamic range image; looking up the RGB color values ​​and the normalized RGB color values ​​corresponding to the target correction coefficient in a four-dimensional lookup table; and using the normalized RGB color values ​​to generate a high dynamic range image corresponding to the standard dynamic range image. The technical solution provided by this application can achieve faster and more efficient conversion of SDR data to HDR data while effectively ensuring image quality.

Methods and systems for dynamic range conversion and display mapping of standard dynamic range (SDR) images onto high dynamic range (HDR) displays are described. Given an SDR input image, a processor generates an intensity (luminance) image and optionally a base layer image and a detail layer image. A first neural network uses the intensity image to predict statistics of the SDR image in a higher dynamic range. These predicted statistics together with the original image statistics of the input image are used to derive an optimal tone-mapping curve to map the input SDR image onto an HDR display. Optionally, a second neural network, using the intensity image and the detail layer image, can generate a residual detail layer image in a higher dynamic range to enhance the tone-mapping of the base layer image into the higher dynamic range.

In a method to improve backwards compatibility when decoding high-dynamic range images coded in a wide color gamut (WCG) space which may not be compatible with legacy color spaces, hue and / or saturation values of images in an image database are computed for both a legacy color space (say, YCbCr-gamma) and a preferred WCG color space (say, IPT-PQ). Based on a cost function, a reshaped color space is computed so that the distance between the hue values in the legacy color space and rotated hue values in the preferred color space is minimized. HDR images are coded in the reshaped color space. Legacy devices can still decode standard dynamic range images assuming they are coded in the legacy color space, while updated devices can use color reshaping information to decode HDR images in the preferred color space at full dynamic range.

The present application relates to the technical field of image processing. Provided in the embodiments are an image processing method and an electronic device. The method comprises: acquiring a first image and a second image, the first image being a high dynamic range (HDR) image, and the second image being a standard dynamic range (SDR) image; on the basis of the first image and the second image, determining a first gain range; on the basis of first gain information and the first gain range, determining second gain information, the first gain information being calculated on the basis of the first image and the second image; and, on the basis of the second gain information and the second image, obtaining a selectable image. In this way, adjusting gain ranges can reduce the quality loss of reconstructed HDR images, thus improving the image quality of images displayed by electronic devices.

To provide a method, an apparatus, a computer program, and a computer program-based device for deriving and signaling a chroma QP (QPc) for high dynamic range (HDR) content.SOLUTION: Quantization parameter signaling mechanisms for both standard dynamic range (SDR) and HDR content in video coding are described using two approaches. The first approach is to send the user-defined Qpc table directly in the high-level syntax. This leads to more flexible and efficient QP control for future codec development and video content coding. A second approach is to signal the luma and chroma QPs independently. This eliminates the need for a Qpc table and eliminates the dependency of the chroma quantization parameter on the luma QP.SELECTED DRAWING: Figure 5A

A high dynamic range (HDR) image processing system is configured to generate visualizations to support digital image processing in both high dynamic range and standard dynamic range (SDR) scenarios. In the first example, the visualization is generated as a histogram. In the second example, the visualization is generated to demonstrate HDR capabilities. In the third example, the visualization is generated to display ranges of luminance values ​​within a digital image. In the fourth example, the visualization is generated as a point curve that defines a mapping between detected luminance values ​​from a digital image and output luminance values ​​across standard and high dynamic range. In the fifth example, the visualization is generated as a preview to convert pixels from the high dynamic range digital image to standard dynamic range.

The invention discloses a video quality determination method, device and equipment and a readable storage medium, and the method comprises the steps: migrating an HDR domain to an SDR domain, combining training optimization, taking a model obtained through SDR video training as a backbone network, and combining a domain migration module to construct a model capable of carrying out the quality evaluation of an HDR video, thereby achieving the quality evaluation of the HDR video. The complexity of model development and maintenance can be reduced, pre-training knowledge of a large-scale standard dynamic range data set can be fully utilized, and dependence on high dynamic range data is reduced.

A method for estimating tone mapping parameters is disclosed. A first tone mapping parameter representative of a white level offset is obtained (S500) for a standard dynamic range current picture. Then, the obtained first tone mapping parameter is compared (S502) with a corresponding first tone mapping parameter obtained for a previous picture. Finally, second tone mapping parameters are updated (S504, S506) for the standard dynamic range current picture responsive to the comparing, wherein the second tone mapping parameters comprise pairs of values representative of a tone mapping output fine tuning function.

The invention discloses an image high dynamic range reconstruction method and system based on a detail coding gain graph, and belongs to the technical field of computer vision and image processing. The method comprises the following steps: S1, data preprocessing: acquiring an input single standard dynamic range image, and preprocessing the image to obtain image data in a linear space or normalized floating point tensor format; s2, proposing a detail recovery network design based on detail generation of a diffusion model; s3, predicting a detail coding gain graph, and providing a gain convolutional neural network design; and S4, final HDR synthesis: carrying out pixel-by-pixel multiplication on the processed standard dynamic range (SDR) image and the three-channel (R, G, B) gain image according to channels to obtain a reconstructed HDR image. According to the method, the problems of detail recovery missing, image structure distortion, low calculation efficiency and the like in the existing single image HDR (High Dynamic Range) reconstruction technology are effectively solved.

One embodiment provides a computer-implemented method that includes employing a de-banding process based on image statistics in a dynamic range conversion system, including high dynamic range (HDR) to standard dynamic range (SDR) tone mapping (TM) process or SDR-to-HDR inverse tone mapping (ITM) process. A computing device performs the de-banding process in post-processing during a determination of one or more initial ITM explicit polynomial curve coefficients. One or more parameters of a weighing function of the one or more initial ITM explicit curve coefficients are utilized as one or more controlling knots for balancing a de-banding effect and preserving local detail.

A method for estimating tone mapping parameters is disclosed. A first tone mapping parameter representative of a white level offset is obtained (S500) for a standard dynamic range current picture. The obtained first tone mapping parameter is then compared (S502) with a corresponding first tone mapping parameter obtained for a previous picture. Finally, in response to the comparison, a second tone mapping parameter is updated (S504, S506) for the standard dynamic range current picture, where the second tone mapping parameter comprises a value pair representative of a tone mapping output trim function.

Embodiments of this application provide a method for determining an image loss and a related apparatus. The method includes: generating a standard dynamic range SDR image based on an original high dynamic range HDR image; generating a reconstructed HDR image based on the original HDR image and the SDR image; and determining image losses of a plurality of areas in the reconstructed HDR image based on the original HDR image and the reconstructed HDR image. In embodiments of this application, a reconstruction loss of the HDR image can be obtained, thereby identifying a visible contour in the reconstructed HDR image.

Various techniques for utilizing gain mapping are disclosed. According to some embodiments, a technique for utilizing gain mapping includes: (1) accessing an enhanced image including a high dynamic range (HDR) image and the gain mapping; (2) extracting the HDR image and the gain mapping from the enhanced image; (3) generating a standard dynamic range (SDR) image using the HDR image and the gain map; (4) receiving a first modification instruction and applying the first modification instruction to the HDR image; (5) generating a second modification instruction at least based on the first modification instruction; (6) applying the second modification instruction to the SDR image; (7) generating a second gain map by comparing the HDR image with the SDR image or vice versa; and (8) embedding the second gain map into the HDR image or the SDR image.