401 results about "Local illumination" patented technology

An apparatus for decoding a video signal comprises a processor, wherein the processor parses a first syntax element that is a general constraint information (GCI) syntax element, parses a second syntax element that indicates whether an LIC mode is available for a current sequence, and parses a third syntax element that indicates whether the LIC mode is used in a current block on the basis of a parsing result of the second syntax element.

A video coding system that uses local illumination compensation to code pixel blocks is provided. A video encoder receives samples for an original block of pixels to be encoded as a current block of a current picture of a video. The video encoder applies a linear model to a reference block to generate a prediction block for the current block. The linear model includes a scale parameter and an offset parameter. The video encoder may use the samples of the original block and samples from a reconstructed reference frame to derive the scale parameter and the offset parameter. The video encoder signals the scale parameter and the offset parameter in a bitstream. The video encoder encodes the current block by using the prediction block to reconstruct the current block.

A method and apparatus to improve compression efficiency in a video compression scheme enables flexible use of local illumination compensation. Such methods comprise individual local illumination compensation for components of a video block. Such methods also comprise flexible derivation methods for illumination compensation information. In one embodiment, local illumination compensation information can be inherited from other others, such as neighboring blocks.

Here provides a system, apparatus, and method(s) for processing images based on colour constancy. The method comprises obtaining an image in a first colour space; converting the image to data in a second colour space; transforming the data using chromatic adaptation; performing a first normalisation on the transformed data, where the first normalisation comprises applying a dynamic spatial filtering technique to adjust the transformed data based on light intensity; applying a set of filters to the normalised data, where the set of filters is convoluted based on the normalised data in relation to the image; performing a second normalisation on the filtered data to obtain an illumination estimation of the image in relation to the filtered data; and outputting the normalised data from the second normalisation, where the normalised data maintains colour constancy based on the illumination estimation, removing the illumination from the normalised data.

The invention provides a stack image enhancement processing method, and relates to the technical field of image enhancement, and the method comprises the steps: collecting and preprocessing a stack image, and constructing a training sample set containing a risk level and an inclination angle label; an encoder-classifier dual-stage model is constructed, an encoder extracts features through a multi-scale feature extraction and enhancement module, a space perception attention enhancement module, a dynamic feature pyramid fusion module and a deformation gradient enhancement residual module, and a classifier outputs a risk level and an inclination angle; during training, classification and regression loss weights are dynamically adjusted, an adversarial enhancement sample is generated to enhance robustness, and a gradient is adaptively cut based on a channel attention enhancement vector. According to the method, local illumination interference can be effectively eliminated, multi-scale features are fused, the sensitivity to a sparse risk area is enhanced, and the self-adaptive enhancement processing effect of the heaped image in a complex environment is improved.

The invention discloses a fluorescence in-situ hybridization image contour enhancement method based on illumination fusion and local binary, relates to the field of image processing, and solves the problems of different dye concentrations and the like due to image defects such as non-uniform local illumination and relatively large noise in an acquired image during existing tissue cell fluorescence image preprocessing. The method comprises the following steps: removing noise in an image by adopting a median filtering method, calculating a linear equation to obtain a texture weight of the image, performing analysis processing by utilizing the texture weight and a smoothing function to obtain a brightness image and a smooth image, and performing gray processing to obtain gray distribution information; adopting a local binary algorithm to select a coefficient, and performing enhancement processing on the image to obtain an illumination enhanced image; the contour feature image and the illumination enhancement image are subjected to weighted addition, eight neighborhoods around eight maximum pixel values are analyzed, an area with a high energy gradient is recognized through a piecewise linear function and a thermodynamic diagram, and a high-frequency signal area is the contour area of the fluorescence in-situ hybridization tissue cell image.

An example device includes one or more processors configured to determine a first search area in a first reference picture for a current block of the video data. The one or more processors are configured to determine a first initial reference block in the first search area. The one or more processors are configured to apply a local illumination compensation model to the first search area to generate a refined first search area. The one or more processors are configured to apply template matching to the refined first search area to determine a first candidate motion vector having a lowest template matching cost for the refined first search area. The one or more processors are configured to decode the current block based on the first candidate motion vector.

A video decoder reconstructs a current frame of a video from a video bitstream based on a reconstructed reference frame. For a block of the current frame, the video decoder identifies a reference block in the reference frame based on a motion vector associated with the block. The decoder determines the slope and offset parameters of a local illumination compensation model based on reconstructed pixels in the current frame and the reference frame. The video decoder decodes, from the video bitstream, an adjustment to the slope and updates the slope by applying the decoded adjustment. The decoder further determines an adjusted offset parameter for the local illumination compensation model. The decoder generates predicted pixels for the block by at least applying, to the reference block, the local illumination compensation model with the updated parameters.

The invention discloses a nighttime image enhancement method based on multidirectional information extraction and color loss optimization, which comprises the following steps: firstly, collecting nighttime and normal light road scene images, constructing a nighttime road image enhancement model based on deep learning, and adopting an image enhancement framework based on GAN and a superficial layer semantic enhanced U-Net network as a generator; the five-layer convolutional network is used as a discriminator, then horizontal-vertical bidirectional differential convolution is used to extract multi-directional information of the image, a local illumination attention module is used to balance dark area brightness and bright area overexposure, and finally an adaptive color loss function is used to recover original colors of the image. The method can effectively solve the problems of image quality reduction, detail loss, color distortion and the like under the low-illumination condition, and is particularly suitable for night scene visual perception improvement in the field of vehicle-assisted automatic driving.

Example devices, methods, and computer-readable media for decoding video data are described. An example method includes determining to decode a current block of the video data using a merge mode. The example method includes generating a first merge list for the current block, wherein generating the first merge list comprises applying template matching to candidates of the first merge list. The example method includes generating, based on the first merge list, a second merge list. The example method includes decoding the current block using the merge mode and based on the first merge list or the second merge list.

Systems, methods, and instrumentalities are disclosed for performing intra template matching prediction (TMP) and local illumination compensation (LIC) in combination. LIC may be used in combination with intra TMP. For example, using LIC (e.g., at the CU level) may be adaptively selected for an intra TMP predicted coding block. LIC may be considered (e.g., used) when performing an intra TMP prediction block search. LIC may be used on some (e.g., not all) intra TMP candidates. Whether to use LIC may be signaled. Performing LIC with intra TMP may be mutually exclusive from performing intra TMP with linear filter model (FLM) mode.

The invention discloses a lighting device with an adjustable optical axis and an optical axis adjusting method thereof. The lighting device comprises a light source module and a lens array which are arranged in parallel. The control module is used for receiving the position information of at least one local lighting area acquired by the position information determination module when the mode determination module determines that the adjustment mode of the lighting device is the first mode, and controlling the optical axis of at least part of the lens units to face the local lighting area according to the position information. And the light emitted by at least part of the lens units is concentrated in the local illumination area. Or when the mode determining module determines that the adjusting mode of the lighting device is the second mode, the light emitted by the light source module is uniformly emitted after passing through the lens array. Thus, the light beam transmission direction and the concentration degree are changed by controlling the optical axis direction of the lens unit, local illumination in the first mode and overall illumination in the second mode are achieved, and therefore the different illumination requirements of the overall area and the local area of indoor illumination are met.

The invention provides a lightweight target detection method and system based on a multi-modal image, and relates to the field of computer vision, and the method comprises the steps: respectively carrying out the convolution, batch normalization and ReLU activation operation of an infrared image and a visible light image to obtain a feature map, then generating a feature weight mask through the convolution operation and a Sigmoid function, and carrying out the recognition of the feature weight mask; performing pixel-by-pixel weighting on the feature map based on the feature weight mask to generate an optimized feature map; extracting illumination perception information through a local illumination perception module so as to perform weighted calculation on the optimized feature map to obtain a weighted feature map, and splicing the visible light weighted feature map and the infrared weighted feature map in the channel dimension to generate a spliced feature map; and performing subsequent feature extraction operation through the residual connection and the parameter-free channel attention module, and inputting the feature extraction operation to the target detection network for target detection so as to output a detection result. According to the method, target detection with higher precision can be realized on the premise of not introducing larger parameter quantity and calculation quantity.

The invention discloses an intelligent directional irradiation module and an illumination control method.The intelligent directional irradiation module comprises a bottom plate, a plurality of LED light source modules, a microwave detection module, an infrared detection module, a lens moving module and a control module are arranged on the bottom plate, and the distance between the LED light source modules and an irradiated object and the outline of the irradiated object are obtained; the received distance data and contour data are compared with preset target parameters, the lens moving module is controlled to work according to a comparison result, the focal length is adjusted, the size of a light-emitting beam of the LED light source module is changed, an illuminated object is highlighted, white balance on the illuminated surface of the object is reduced, and overexposure is controlled; therefore, the local detail features of the illuminated object are clearly presented, and the illumination effect and the illumination quality of local illumination are improved.

The invention discloses an airport video image classification method based on artificial intelligence, and relates to the technical field of video image classification, and the method comprises the steps: carrying out the whole-course video monitoring of an airport apron region, including a red line region, a stop line and an airplane entry line; carrying out real-time splicing fusion processing on pictures obtained by the video monitoring software by using a video image splicing method; performing frame-by-frame separation on the panoramic video image to obtain each image, namely an airport apron gate panoramic image; a panoramic image of an airport apron parking space is classified, and an illumination perception multi-scale feature pyramid network is used as a core architecture. According to the method, the stability of feature expression is enhanced by fusing local illumination statistics and global channel attention by adopting an adaptive normalization method. According to the method, the deformable convolution kernel group and the scale are adopted to perceive attention on the basis of the feature pyramid network, the receptive field can be adaptively adjusted, the multi-scale features are dynamically fused, and the cross-scale space correlation capturing capability in the image is improved.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a video unit of a video and a bitstream of the video unit, a Local illumination compensation (LIC) type of the video unit based on at least one neighbouring reconstructed sample of the video unit; and performing the conversion based on the determined LIC type.

A device may perform local illumination compensation. The device may determine whether local illumination compensation is to be performed for a luma component of a video block. The device may determine a value of a chroma local illumination compensation variable based on local illumination compensation being performed for a luma component of a video block and one or more thresholds. The chroma local illumination compensation variable may indicate whether local illumination compensation is to be performed for a chroma component of the video block.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: obtaining, for a conversion between a current video block of a video and a bitstream of the video, values for a set of adjusting parameters associated with values for a set of model parameters of a local illumination compensation (LIC) model for coding the current video block; updating the values for the set of model parameters based on the values for the set of adjusting parameters; and performing the conversion based on the updated values for the set of model parameters.

A non-transitory machine-readable medium of an electronic device storing computer-executable instructions for decoding a bitstream is provided. The computer-executable instructions, when executed by a processor of the electronic device, cause the electronic device to receive an image frame of the bitstream; determine a first mode from a plurality of local illumination compensation (LIC) modes; determine a first template of a block unit of the image frame and at least one second template of at least one reference block based on the first mode; determine a first model based on the first template and the at least one second template; obtain a slope adjustment parameter; adjust the first model based on the slope adjustment parameter; and modify a prediction illumination of the block unit of the image frame based on the adjusted first model.

A sensing system may include an illumination projector, an image sensor, and one or more controllers. The illumination projector may project a partial illumination pattern on a target object. The image sensor may sense the partial illumination pattern projected on the target object and may generate signals representative of the sensed illumination pattern. The one or more controllers may control the illumination projector and process the signals generated by the image sensor. The illumination projector may include a microlens array and an emitter array. A spatial relationship of emitters of the emitter array to microlenses of the microlens array may cause the emitter array and the microlens array to project light upon the target object in the partial illumination pattern.

A method and an apparatus for video decoding are provided. The apparatus includes processing circuitry that decodes prediction information of one or more blocks including a current block from a coded video bitstream. The prediction information indicates that local illumination compensation (LIC) is applied to the one or more blocks and includes LIC information of the one or more blocks. The processing circuitry determines a final scaling factor α_f1 and a final offset β_f1 of a first subblock in the current block based on the LIC information. The processing circuitry determines an updated first predictor subblock in a predictor corresponding to the current block based on the final scaling factor α_f1, the final offset β_f1, and the first predictor subblock. An updated value of a first sample in the first predictor subblock is equal to α_f1×p_i1+β_f1. p_i1 is a value of the first sample.

Example devices, methods, and computer-readable media are described. An example method includes determining to decode a current block of the video data using a merge mode. The example method includes determining, for the current block, to apply local illumination compensation (LIC). The example method includes determining, for the current block, to apply decoder side motion vector refinement (DMVR). The example method includes decoding the current block based on applying LIC and applying DMVR.

A device may perform local illumination compensation. The device may determine whether local illumination compensation is to be performed for a luma component of a video block. The device determine a value of a chroma local illumination compensation variable based on local illumination compensation being performed for a luma component of a video block. The chroma local illumination compensation variable may indicate whether local illumination compensation is to be performed for a chroma component of the video block.

The invention provides an image enhancement method based on double-domain illumination prior and electronic equipment, and relates to the field of image processing. The method comprises the steps of obtaining a to-be-processed illumination image; according to local illumination distribution characteristics of the to-be-processed illumination image, multi-scale enhancement is carried out on the to-be-processed illumination image after global illumination enhancement, a first illumination image after spatial domain enhancement is obtained, and the local illumination distribution characteristics comprise a brightness mean value and a brightness standard deviation; amplitude information of the to-be-processed illumination image in the frequency domain is enhanced, and a second illumination image enhanced in the frequency domain is obtained; and fusing the to-be-processed illumination image, the first illumination image and the second illumination image to obtain an enhanced target illumination image.

Disclosed is a mirror light, including a first light body with a built-in spatial lighting module; and a second light body with a built-in local lighting module. In use, the first light body performs spatial lighting, and the second light body performs local lighting, thereby meeting different lighting needs of users.

A video coder receives a Local Illumination Compensation (LIC) linear model that is generated using samples neighboring two different blocks. The linear model includes a scale factor and a y-intercept. The video coder adjusts the linear model by applying an offset to adjust the scaling factor (or slope) of the linear model. The scale factor or the y-intercept of the adjusted linear model may be clipped to stay within a defined range. The offset may be selected from a set of predefined values based on a syntax element that is signaled in a bitstream. The applied offset may be inherited from a previously coded position. When the current block is coded by bi-prediction, the linear model may be one of first and second linear models used to generate a predictor of the current block.

An example device includes memory, and one or more processors configured to determine to decode a current block of the video data using motion information of a second block of the video data. The one or more processors are configured to determine a value of a LIC flag of the second block, the value of the LIC flag of the second block indicative of LIC being applied to the second block. The one or more processors are configured to determine, based on the value of the LIC flag, to apply LIC to the current block. The one or more processors are configured to infer LIC model parameters of the current block to be equal to LIC model parameters of the second block. The one or more processors are configured to decode the current block including using the inferred LIC model parameters and the motion information of the second block.

The invention relates to the field of underground illumination management, and discloses an underground multi-mode illumination collaborative management method and system in a red-mine scene, and the method comprises the steps: capturing the personnel distribution, equipment operation state, environment change and local illumination condition in a working region in real time, employing a time sequence prediction model based on deep learning, and carrying out the real-time monitoring of the operation region, the method comprises the following steps: analyzing and identifying local illumination requirements of different operation areas, predicting an insufficient illumination phenomenon occurring in a short time in the future in combination with a personnel moving track, an equipment moving area and an environmental condition, dynamically adjusting the working state of each illumination unit, calculating a control scheme of each illumination unit by utilizing a collaborative scheduling technology, and performing real-time control. And the output power of the lighting system is dynamically adjusted, an abnormal feedback mechanism is automatically triggered, the dimming strategy is recalculated and adaptive adjustment is carried out, and the effect of the current lighting dimming strategy is analyzed by using a multi-dimensional evaluation method. The method has the advantage of improving the safety of mine operation.

The invention relates to the technical field of illumination, and discloses a plant light supplementing illumination device which comprises an illumination assembly, an angle adjusting assembly, a distance adjusting assembly and a connecting assembly, the illumination assembly is used for illuminating plants, and the connecting assembly is used for connecting the illumination assembly and the distance adjusting assembly. The included angle of the second reflecting plate in each lighting assembly can be conveniently adjusted through the angle adjusting assembly, so that the irradiation range of the lighting lamp below the first reflecting plate is changed, the included angle between the second reflecting plates can be conveniently reduced to facilitate storage, the distance between the lighting assemblies can be conveniently changed through the distance adjusting assembly and the connecting assembly, and the lighting effect is improved. The illumination range of the illumination lamp can be conveniently changed by changing the distance between the illumination assemblies, the three illumination lamps can be conveniently matched with the angle adjusting assembly, the overall and local illumination range is changed, adjustment is more flexible, and meanwhile storage is more facilitated by changing the distance between the illumination lamps.

The invention provides an unmanned aerial vehicle remote sensing monitoring and evaluating method and system for field orchard diseases and insect pests, and relates to the technical field of orchard disease and insect pest monitoring and evaluating. The interference of complex background noise is effectively eliminated by constructing a ternary separation index integrating feature enhancement, background suppression and brightness normalization; a threshold value is determined based on the exponential distribution, pixels are accurately divided into a non-canopy background, a shadow canopy and an illumination canopy, and fine segmentation of an unstructured scene is achieved; constructing a gain compensation item by using a local illumination reference mean value, correcting a shadow canopy reflectivity curve, and eliminating illumination non-uniform interference; selecting a local high quantile to establish a health reference basis, coupling a discriminant item and an attenuation item, and resolving to obtain a physiological stress index, so as to realize acute capture of early weak disease characteristics; and performing power weighted accumulation on the abnormal pixel difference value, outputting a regional hazard level index, and objectively quantifying the overall disaster risk of the region through nonlinear aggregation.