Patent ID: 12231782

The figures depict, and the detail description describes various non-limiting embodiments for purposes of illustration only.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, the described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

Embodiments of the present disclosure relate to an image signal processor that performs multi-illumination white balance (MIWB) on image data for, e.g., correcting colors of shadows in an input image, where the shadows are generated from different (ambient) light sources. Colors of shadows in image data generated from different light sources may deviate from actual colors (e.g., perceived by the human eye). By applying the MIWB on image data, the colors of shadows can be corrected. The MIWB is based on multi-illuminant processing of a thumbnail image (e.g., downscaled version of the input image). A weight (illuminant) map of the thumbnail image may be first determined, where each weight in the weight map represents one intensity level of a respective chrominance class (e.g., warm, cool, and neutral chrominance class) for a source pixel in the thumbnail image. The weight map may be applied to component values of color channels of source pixels in the thumbnail image to generate component values of the color channels of target pixels in a target thumbnail image with corrected colors of shadows. A preferred look from the target thumbnail image can be then translated to a full resolution image.

Exemplary Electronic Device

Embodiments of electronic devices, user interfaces for such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as personal digital assistant (PDA) and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, Apple Watch®, and iPad® devices from Apple Inc. of Cupertino, California. Other portable electronic devices, such as wearables, laptops or tablet computers, are optionally used. In some embodiments, the device is not a portable communication device, but is a desktop computer or other computing device that is not designed for portable use. In some embodiments, the disclosed electronic device may include a touch-sensitive surface (e.g., a touch screen display and/or a touchpad). An example electronic device described below in conjunction with Figure (FIG.1(e.g., device100) may include a touch-sensitive surface for receiving user input. The electronic device may also include one or more other physical user-interface devices, such as a physical keyboard, a mouse and/or a joystick.

FIG.1is a high-level diagram of an electronic device100, according to one embodiment. Device100may include one or more physical buttons, such as a “home” or menu button104. Menu button104is, for example, used to navigate to any application in a set of applications that are executed on device100. In some embodiments, menu button104includes a fingerprint sensor that identifies a fingerprint on menu button104. The fingerprint sensor may be used to determine whether a finger on menu button104has a fingerprint that matches a fingerprint stored for unlocking device100. Alternatively, in some embodiments, menu button104is implemented as a soft key in a graphical user interface (GUI) displayed on a touch screen.

In some embodiments, device100includes touch screen150, menu button104, push button106for powering the device on/off and locking the device, volume adjustment buttons108, Subscriber Identity Module (SIM) card slot110, head set jack112, and docking/charging external port124. Push button106may be used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device100also accepts verbal input for activation or deactivation of some functions through microphone113. Device100includes various components including, but not limited to, a memory (which may include one or more computer readable storage mediums), a memory controller, one or more central processing units (CPUs), a peripherals interface, an RF circuitry, an audio circuitry, speaker111, microphone113, input/output (I/O) subsystem, and other input or control devices. Device100may include one or more image sensors164, one or more proximity sensors166, and one or more accelerometers168. Device100may include more than one type of image sensors164. Each type may include more than one image sensor164. For example, one type of image sensors164may be cameras and another type of image sensors164may be infrared sensors that may be used for face recognition. Additionally or alternatively, image sensors164may be associated with different lens configuration. For example, device100may include rear image sensors, one with a wide-angle lens and another with as a telephoto lens. Device100may include components not shown inFIG.1such as an ambient light sensor, a dot projector and a flood illuminator.

Device100is only one example of an electronic device, and device100may have more or fewer components than listed above, some of which may be combined into a component or have a different configuration or arrangement. The various components of device100listed above are embodied in hardware, software, firmware or a combination thereof, including one or more signal processing and/or application specific integrated circuits (ASICs). While the components inFIG.1are shown as generally located on the same side as the touch screen150, one or more components may also be located on an opposite side of device100. For example, the front side of device100may include an infrared image sensor164for face recognition and another image sensor164as the front camera of device100. The back side of device100may also include additional two image sensors164as the rear cameras of device100.

FIG.2is a block diagram illustrating components in device100, according to one embodiment. Device100may perform various operations including image processing. For this and other purposes, the device100may include, among other components, image sensors202, system-on-a chip (SOC) component204, system memory230, persistent storage (e.g., flash memory)228, motion sensor234, and display216. The components as illustrated inFIG.2are merely illustrative. For example, device100may include other components (such as speaker or microphone) that are not illustrated inFIG.2. Further, some components (such as motion sensor234) may be omitted from device100.

Image sensors202are components for capturing image data. Each of image sensors202may be embodied, for example, as a complementary metal-oxide-semiconductor (CMOS) active-pixel sensor, a camera, video camera, or other devices. Image sensors202generate raw image data that is sent to SOC component204for further processing. In some embodiments, the image data processed by SOC component204is displayed on display216, stored in system memory230, persistent storage228or sent to a remote computing device via network connection. The raw image data generated by image sensors202may be in a Bayer color filter array (CFA) pattern (hereinafter also referred to as “Bayer pattern”). Image sensor202may also include optical and mechanical components that assist image sensing components (e.g., pixels) to capture images. The optical and mechanical components may include an aperture, a lens system, and an actuator that controls the focal length of image sensor202.

Motion sensor234is a component or a set of components for sensing motion of device100. Motion sensor234may generate sensor signals indicative of orientation and/or acceleration of device100. The sensor signals are sent to SOC component204for various operations such as turning on device100or rotating images displayed on display216.

Display216is a component for displaying images as generated by SOC component204. Display216may include, for example, a liquid crystal display (LCD) device or an organic light emitting diode (OLED) device. Based on data received from SOC component204, display216may display various images, such as menus, selected operating parameters, images captured by image sensors202and processed by SOC component204, and/or other information received from a user interface of device100(not shown).

System memory230is a component for storing instructions for execution by SOC component204and for storing data processed by SOC component204. System memory230may be embodied as any type of memory including, for example, dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate (DDR, DDR2, DDR3, etc.) RAMBUS DRAM (RDRAM), static RAM (SRAM) or a combination thereof. In some embodiments, system memory230may store pixel data or other image data or statistics in various formats.

Persistent storage228is a component for storing data in a non-volatile manner. Persistent storage228retains data even when power is not available. Persistent storage228may be embodied as read-only memory (ROM), flash memory or other non-volatile random access memory devices.

SOC component204is embodied as one or more integrated circuit (IC) chip and performs various data processing processes. SOC component204may include, among other subcomponents, image signal processor (ISP)206, a central processor unit (CPU)208, a network interface210, motion sensor interface212, display controller214, graphics processor unit (GPU)220, memory controller222, video encoder224, storage controller226, and various other input/output (I/O) interfaces218, and bus232connecting these subcomponents. SOC component204may include more or fewer subcomponents than those shown inFIG.2.

ISP206is hardware that performs various stages of an image processing pipeline. In some embodiments, ISP206may receive raw image data from image sensors202, and process the raw image data into a form that is usable by other subcomponents of SOC component204or components of device100. ISP206may perform various image-manipulation operations such as image translation operations, horizontal and vertical scaling, color space conversion and/or image stabilization transformations, as described below in detail with reference toFIG.3.

CPU208may be embodied using any suitable instruction set architecture, and may be configured to execute instructions defined in that instruction set architecture. CPU208may be general-purpose or embedded processors using any of a variety of instruction set architectures (ISAs), such as the ×86, PowerPC, SPARC, RISC, ARM or MIPS ISAs, or any other suitable ISA. Although a single CPU is illustrated inFIG.2, SOC component204may include multiple CPUs. In multiprocessor systems, each of the CPUs may commonly, but not necessarily, implement the same ISA.

GPU220is graphics processing circuitry for performing operations on graphical data. For example, GPU220may render objects to be displayed into a frame buffer (e.g., one that includes pixel data for an entire frame). GPU220may include one or more graphics processors that may execute graphics software to perform a part or all of the graphics operation, or hardware acceleration of certain graphics operations.

I/O interfaces218are hardware, software, firmware or combinations thereof for interfacing with various input/output components in device100. I/O components may include devices such as keypads, buttons, audio devices, and sensors such as a global positioning system. I/O interfaces218process data for sending data to such I/O components or process data received from such I/O components.

Network interface210is a subcomponent that enables data to be exchanged between devices100and other devices via one or more networks (e.g., carrier or agent devices). For example, video or other image data may be received from other devices via network interface210and be stored in system memory230for subsequent processing (e.g., via a back-end interface to image signal processor206, such as discussed below inFIG.3) and display. The networks may include, but are not limited to, Local Area Networks (LANs) (e.g., an Ethernet or corporate network) and Wide Area Networks (WANs). The image data received via network interface210may undergo image processing processes by ISP206.

Motion sensor interface212is circuitry for interfacing with motion sensor234. Motion sensor interface212receives sensor information from motion sensor234and processes the sensor information to determine the orientation or movement of the device100.

Display controller214is circuitry for sending image data to be displayed on display216. Display controller214receives the image data from ISP206, CPU208, graphic processor or system memory230and processes the image data into a format suitable for display on display216.

Memory controller222is circuitry for communicating with system memory230. Memory controller222may read data from system memory230for processing by ISP206, CPU208, GPU220or other subcomponents of SOC component204. Memory controller222may also write data to system memory230received from various subcomponents of SOC component204.

Video encoder224is hardware, software, firmware or a combination thereof for encoding video data into a format suitable for storing in persistent storage228or for passing the data to network interface210for transmission over a network to another device.

In some embodiments, one or more subcomponents of SOC component204or some functionality of these subcomponents may be performed by software components executed on ISP206, CPU208or GPU220. Such software components may be stored in system memory230, persistent storage228or another device communicating with device100via network interface210.

Image data or video data may flow through various data paths within SOC component204. In one example, raw image data may be generated from image sensors202and processed by ISP206, and then sent to system memory230via bus232and memory controller222. After the image data is stored in system memory230, it may be accessed by video encoder224for encoding or by display216for displaying via bus232.

In another example, image data is received from sources other than image sensors202. For example, video data may be streamed, downloaded, or otherwise communicated to the SOC component204via wired or wireless network. The image data may be received via network interface210and written to system memory230via memory controller222. The image data may then be obtained by ISP206from system memory230and processed through one or more image processing pipeline stages, as described below in detail with reference toFIG.3. The image data may then be returned to system memory230or be sent to video encoder224, display controller214(for display on display216), or storage controller226for storage at persistent storage228.

Example Image Signal Processing Pipelines

FIG.3is a block diagram illustrating image processing pipelines implemented using ISP206, according to one embodiment. In the embodiment ofFIG.3, ISP206is coupled to an image sensor system201that includes one or more image sensors202A through202N (hereinafter collectively referred to as “image sensors202” or also referred individually as “image sensor202”) to receive raw image data. Image sensor system201may include one or more sub-systems that control image sensors202individually. In some cases, each image sensor202may operate independently while, in other cases, image sensors202may share some components. For example, in one embodiment, two or more image sensors202may share the same circuit board that controls the mechanical components of the image sensors (e.g., actuators that change the focal lengths of each image sensor). The image sensing components of image sensor202may include different types of image sensing components that may provide raw image data in different forms to ISP206. For example, in one embodiment, the image sensing components may include multiple focus pixels that are used for auto-focusing and multiple image pixels that are used for capturing images. In another embodiment, the image sensing pixels may be used for both auto-focusing and image capturing purposes.

ISP206implements an image processing pipeline which may include a set of stages that process image information from creation, capture or receipt to output. ISP206may include, among other components, sensor interface302, central control320, front-end pipeline stages330, back-end pipeline stages340, image statistics module304, vision module322, back-end interface342, output interface316, and auto-focus circuits350A through350N (hereinafter collectively referred to as “auto-focus circuits350” or referred individually as “auto-focus circuits350”). ISP206may include other components not illustrated inFIG.3or may omit one or more components illustrated inFIG.3.

In one or more embodiments, different components of ISP206process image data at different rates. In the embodiment ofFIG.3, front-end pipeline stages330(e.g., raw processing stage306and resample processing stage308) may process image data at an initial rate. Thus, the various different techniques, adjustments, modifications, or other processing operations performed by these front-end pipeline stages330at the initial rate. For example, if front-end pipeline stages330process two pixels per clock cycle, then raw processing stage306operations (e.g., black level compensation, highlight recovery and defective pixel correction) may process two pixels of image data at a time. In contrast, one or more back-end pipeline stages340may process image data at a different rate less than the initial data rate. For example, in the embodiment ofFIG.3, back-end pipeline stages340(e.g., noise processing stage310, color processing stage312, and output rescale314) may be processed at a reduced rate (e.g., one pixel per clock cycle).

Raw image data captured by image sensors202may be transmitted to different components of ISP206in different manners. In one embodiment, raw image data corresponding to the focus pixels may be sent to auto-focus circuits350while raw image data corresponding to the image pixels may be sent to sensor interface302. In another embodiment, raw image data corresponding to both types of pixels may simultaneously be sent to both auto-focus circuits350and sensor interface302.

Auto-focus circuits350may include hardware circuit that analyzes raw image data to determine an appropriate focal length of each image sensor202. In one embodiment, the raw image data may include data that is transmitted from image sensing pixels that specializes in image focusing. In another embodiment, raw image data from image capture pixels may also be used for auto-focusing purpose. Auto-focus circuit350may perform various image processing operations to generate data that determines the appropriate focal length. The image processing operations may include cropping, binning, image compensation, scaling to generate data that is used for auto-focusing purpose. The auto-focusing data generated by auto-focus circuits350may be fed back to image sensor system201to control the focal lengths of image sensors202. For example, image sensor202may include a control circuit that analyzes the auto-focusing data to determine a command signal that is sent to an actuator associated with the lens system of image sensor202to change the focal length of image sensor202. The data generated by auto-focus circuits350may also be sent to other components of ISP206for other image processing purposes. For example, some of the data may be sent to image statistics module304to determine information regarding auto-exposure.

Auto-focus circuits350may be individual circuits that are separate from other components such as image statistics module304, sensor interface302, front-end330and back-end340. This allows ISP206to perform auto-focusing analysis independent of other image processing pipelines. For example, ISP206may analyze raw image data from image sensor202A to adjust the focal length of image sensor202A using auto-focus circuit350A while performing downstream image processing of the image data from image sensor202B simultaneously. In one embodiment, the number of auto-focus circuits350may correspond to the number of image sensors202. In other words, each image sensor202may have a corresponding auto-focus circuit that is dedicated to the auto-focusing of image sensor202. Device100may perform auto focusing for different image sensors202even if one or more image sensors202are not in active use. This allows a seamless transition between two image sensors202when device100switches from one image sensor202to another. For example, in one embodiment, device100may include a wide-angle camera and a telephoto camera as a dual back camera system for photo and image processing. Device100may display images captured by one of the dual cameras and may switch between the two cameras from time to time. The displayed images may seamlessly transition from image data captured by one image sensor202to image data captured by another image sensor202without waiting for second image sensor202to adjust its focal length because two or more auto-focus circuits350may continuously provide auto-focus data to image sensor system201.

Raw image data captured by different image sensors202may also be transmitted to sensor interface302. Sensor interface302receives raw image data from image sensors202and processes the raw image data into an image data processable by other stages in the pipeline. Sensor interface302may perform various preprocessing operations, such as image cropping, binning or scaling to reduce image data size. In some embodiments, pixels are sent from image sensors202to sensor interface302in raster order (e.g., horizontally, line by line). The subsequent processes in the pipeline may also be performed in raster order and the result may also be output in raster order. Although only a single image sensor system201and a single sensor interface302are illustrated inFIG.3, when more than one image sensor system is provided in device100, a corresponding number of sensor interfaces may be provided in ISP206to process raw image data from each image sensor system.

Front-end pipeline stages330process image data in raw or full-color domains. Front-end pipeline stages330may include, but are not limited to, raw processing stage306and resample processing stage308. A raw image data may be in a Bayer raw image format, for example. In the Bayer raw image format, pixel data with values specific to a particular color (instead of all colors) is provided in each pixel. In an image capturing sensor, image data is typically provided in the Bayer pattern. Raw processing stage306may process image data in the Bayer raw image format.

The operations performed by raw processing stage306include, but are not limited, sensor linearization, black level compensation, fixed pattern noise reduction, defective pixel correction, raw noise filtering, lens shading correction, white balance gain, highlight recovery, and chromatic aberration recovery (or correction). Sensor linearization refers to mapping non-linear image data to linear space for other processing. Black level compensation refers to providing digital gain, offset and clip independently for each color component (e.g., Gr, R, B, Gb) of the image data. Fixed pattern noise reduction refers to removing offset fixed pattern noise and gain fixed pattern noise by subtracting a dark frame from an input image and multiplying different gains to pixels. Defective pixel correction refers to detecting defective pixels, and then replacing defective pixel values. Raw noise filtering refers to reducing noise of image data by averaging neighbor pixels that are similar in brightness. Highlight recovery refers to estimating pixel values for those pixels that are clipped (or nearly clipped) from other channels. Lens shading correction refers to applying a gain per pixel to compensate for a dropoff in intensity roughly proportional to a distance from a lens optical center. White balance gain refers to providing digital gains for white balance, offset and clip independently for all color components (e.g., Gr, R, B, Gb in the Bayer pattern).

Components of ISP206may convert raw image data into image data in full-color domain, and thus, raw processing stage306may process image data in the full-color domain in addition to or instead of raw image data.

Resample processing stage308performs various operations to convert, resample, or scale image data received from raw processing stage306. Operations performed by resample processing stage308may include, but not limited to, demosaic operation, per-pixel color correction operation, Gamma mapping operation, color space conversion and downscaling or sub-band splitting. Demosaic operation refers to converting or interpolating missing color samples from raw image data (for example, in the Bayer pattern) to output image data into a full-color domain. Demosaic operation may include low pass directional filtering on the interpolated samples to obtain full-color pixels. Per-pixel color correction operation refers to a process of performing color correction on a per-pixel basis using information about relative noise standard deviations of each color channel to correct color without amplifying noise in the image data. Gamma mapping refers to converting image data from input image data values to output data values to perform gamma correction. For the purpose of Gamma mapping, lookup tables (or other structures that index pixel values to another value) for different color components or channels of each pixel (e.g., a separate lookup table for R, G, and B color components) may be used. Color space conversion refers to converting color space of an input image data into a different format. In one embodiment, resample processing stage308converts RGB format into YCbCr format for further processing. In another embodiment, resample processing stage308concerts RBD format into RGB format for further processing.

Central control module320may control and coordinate overall operation of other components in ISP206. Central control module320performs operations including, but not limited to, monitoring various operating parameters (e.g., logging clock cycles, memory latency, quality of service, and state information), updating or managing control parameters for other components of ISP206, and interfacing with sensor interface302to control the starting and stopping of other components of ISP206. For example, central control module320may update programmable parameters for other components in ISP206while the other components are in an idle state. After updating the programmable parameters, central control module320may place these components of ISP206into a run state to perform one or more operations or tasks. Central control module320may also instruct other components of ISP206to store image data (e.g., by writing to system memory230inFIG.2) before, during, or after resample processing stage308. In this way full-resolution image data in raw or full-color domain format may be stored in addition to or instead of processing the image data output from resample processing stage308through backend pipeline stages340.

Image statistics module304performs various operations to collect statistic information associated with the image data. The operations for collecting statistics information may include, but not limited to, sensor linearization, replace patterned defective pixels, sub-sample raw image data, detect and replace non-patterned defective pixels, black level compensation, lens shading correction, and inverse black level compensation. After performing one or more of such operations, statistics information such as 3A statistics (auto white balance (AWB), auto exposure (AE), histograms (e.g., 2D color or component) and any other image data information may be collected or tracked. In some embodiments, certain pixels' values, or areas of pixel values may be excluded from collections of certain statistics data when preceding operations identify clipped pixels. Although only a single statistics module304is illustrated inFIG.3, multiple image statistics modules may be included in ISP206. For example, each image sensor202may correspond to an individual image statistics module304. In such embodiments, each statistic module may be programmed by central control module320to collect different information for the same or different image data.

Vision module322performs various operations to facilitate computer vision operations at CPU208such as facial detection in image data. Vision module322may perform various operations including pre-processing, global tone-mapping and Gamma correction, vision noise filtering, resizing, keypoint detection, generation of histogram-of-orientation gradients (HOG) and normalized cross correlation (NCC). The pre-processing may include subsampling or binning operation and computation of luminance if the input image data is not in YCrCb format. Global mapping and Gamma correction can be performed on the pre-processed data on luminance image. Vision noise filtering is performed to remove pixel defects and reduce noise present in the image data, and thereby, improve the quality and performance of subsequent computer vision algorithms. Such vision noise filtering may include detecting and fixing dots or defective pixels, and performing bilateral filtering to reduce noise by averaging neighbor pixels of similar brightness. Various vision algorithms use images of different sizes and scales. Resizing of an image is performed, for example, by binning or linear interpolation operation. Keypoints are locations within an image that are surrounded by image patches well suited to matching in other images of the same scene or object. Such keypoints are useful in image alignment, computing camera pose and object tracking. Keypoint detection refers to the process of identifying such keypoints in an image. HOG provides descriptions of image patches for tasks in mage analysis and computer vision. HOG can be generated, for example, by (i) computing horizontal and vertical gradients using a simple difference filter, (ii) computing gradient orientations and magnitudes from the horizontal and vertical gradients, and (iii) binning the gradient orientations. NCC is the process of computing spatial cross-correlation between a patch of image and a kernel.

Back-end interface342receives image data from other image sources than image sensor202and forwards it to other components of ISP206for processing. For example, image data may be received over a network connection and be stored in system memory230. Back-end interface342retrieves the image data stored in system memory230and provides it to back-end pipeline stages340for processing. One of many operations that are performed by back-end interface342is converting the retrieved image data to a format that can be utilized by back-end processing stages340. For instance, back-end interface342may convert RGB, YCbCr 4:2:0, or YCbCr 4:2:2 formatted image data into YCbCr 4:4:4 color format.

Back-end pipeline stages340processes image data according to a particular full-color format (e.g., YCbCr 4:4:4 or RGB). In some embodiments, components of the back-end pipeline stages340may convert image data to a particular full-color format before further processing. Back-end pipeline stages340may include, among other stages, noise processing stage310and color processing stage312. Back-end pipeline stages340may include other stages not illustrated inFIG.3.

Noise processing stage310performs various operations to reduce noise in the image data. The operations performed by noise processing stage310include, but are not limited to, color space conversion, gamma/de-gamma mapping, temporal filtering, noise filtering, luma sharpening, and chroma noise reduction. The color space conversion may convert an image data from one color space format to another color space format (e.g., RGB format converted to YCbCr format). Gamma/de-gamma operation converts image data from input image data values to output data values to perform gamma correction or reverse gamma correction. Temporal filtering filters noise using a previously filtered image frame to reduce noise. For example, pixel values of a prior image frame are combined with pixel values of a current image frame. Noise filtering may include, for example, spatial noise filtering. Luma sharpening may sharpen luma values of pixel data while chroma suppression may attenuate chroma to gray (e.g., no color). In some embodiment, the luma sharpening and chroma suppression may be performed simultaneously with spatial nose filtering. The aggressiveness of noise filtering may be determined differently for different regions of an image. Spatial noise filtering may be included as part of a temporal loop implementing temporal filtering. For example, a previous image frame may be processed by a temporal filter and a spatial noise filter before being stored as a reference frame for a next image frame to be processed. In other embodiments, spatial noise filtering may not be included as part of the temporal loop for temporal filtering (e.g., the spatial noise filter may be applied to an image frame after it is stored as a reference image frame and thus the reference frame is not spatially filtered.

Color processing stage312may perform various operations associated with adjusting color information in the image data. The operations performed in color processing stage312include, but are not limited to, local tone mapping, gain/offset/clip, color correction, three-dimensional color lookup, gamma conversion, and color space conversion. Local tone mapping refers to spatially varying local tone curves in order to provide more control when rendering an image. For instance, a two-dimensional grid of tone curves (which may be programmed by central control module320) may be bilinearly interpolated such that smoothly varying tone curves are created across an image. In some embodiments, local tone mapping may also apply spatially varying and intensity varying color correction matrices, which may, for example, be used to make skies bluer while turning down blue in the shadows in an image. Digital gain/offset/clip may be provided for each color channel or component of image data. Color correction may apply a color correction transform matrix to image data. 3D color lookup may utilize a three-dimensional array of color component output values (e.g., R, G, B) to perform advanced tone mapping, color space conversions, and other color transforms. Gamma conversion may be performed, for example, by mapping input image data values to output data values in order to perform gamma correction, tone mapping, or histogram matching. Color space conversion may be implemented to convert image data from one color space to another (e.g., RGB to YCbCr). Other processing techniques may also be performed as part of color processing stage312to perform other special image effects, including black and white conversion, sepia tone conversion, negative conversion, or solarize conversion.

Output rescale module314may resample, transform, and correct distortion on the fly as ISP206processes image data. Output rescale module314may compute a fractional input coordinate for each pixel and uses this fractional coordinate to interpolate an output pixel via a polyphase resampling filter. A fractional input coordinate may be produced from a variety of possible transforms of an output coordinate, such as resizing or cropping an image (e.g., via a simple horizontal and vertical scaling transform), rotating and shearing an image (e.g., via non-separable matrix transforms), perspective warping (e.g., via an additional depth transform) and per-pixel perspective divides applied in piecewise in strips to account for changes in image sensor during image data capture (e.g., due to a rolling shutter), and geometric distortion correction (e.g., via computing a radial distance from the optical center in order to index an interpolated radial gain table, and applying a radial perturbance to a coordinate to account for a radial lens distortion).

Output rescale module314may apply transforms to image data as it is processed at output rescale module314. Output rescale module314may include horizontal and vertical scaling components. The vertical portion of the design may implement series of image data line buffers to hold the “support” needed by the vertical filter. As ISP206may be a streaming device, it may be that only the lines of image data in a finite-length sliding window of lines are available for the filter to use. Once a line has been discarded to make room for a new incoming line, the line may be unavailable. Output rescale module314may statistically monitor computed input Y coordinates over previous lines and use it to compute an optimal set of lines to hold in the vertical support window. For each subsequent line, output rescale module may automatically generate a guess as to the center of the vertical support window. In some embodiments, the output rescale module314may implement a table of piecewise perspective transforms encoded as digital difference analyzer (DDA) steppers to perform a per-pixel perspective transformation between an input image data and output image data in order to correct artifacts and motion caused by sensor motion during the capture of the image frame. Output rescale may provide image data via output interface316to various other components of device100, as discussed above in relation toFIGS.1and2.

In various embodiments, the functionally of components302through350may be performed in a different order than the order implied by the order of these functional units in the image processing pipeline illustrated inFIG.3or may be performed by different functional components than those illustrated inFIG.3. Moreover, the various components as described inFIG.3may be embodied in various combinations of hardware, firmware, or software.

Example Multi-Illumination White Balance Circuit with Thumbnail Image Processing

FIG.4is a block diagram illustrating a multi-illumination white balance (MIWB) circuit400with a thumbnail image processing circuit406and a reintegration circuit416as part of color processing stage312, according to one embodiment. The MIWB circuit400may perform MIWB on image data for, e.g., correcting colors of shadows in an image that may be generated from different ambient light sources. Colors of shadows in image data generated from different light sources may deviate from actual colors. For example, shadow areas may have a different illuminant than bright areas. The shadows may be bluer due to light from the blue sky, while the highlights may be more yellow from direct illumination from the sun. The MIWB presented herein is designed to handle such mixed illuminant cases so that, e.g., the blue color component is attenuated more in the shadows. The MIWB processing of image data may include estimation of different color intensities (e.g., color temperatures) in image data by, e.g., collecting color information about white objects, and adjusting color gains to compensate the image data for incorrect colors (e.g., of shadows). The MIWB circuit400may include a thumbnail image processing circuit406, a resizing circuit420(optional) coupled to outputs of thumbnail image processing circuit406, a thumbnail output direct memory access (DMA) circuit428coupled to outputs of resizing circuit420(or outputs of thumbnail image processing circuit406when resizing circuit420is bypassed), and a reintegration circuit416coupled to thumbnail image processing circuit406via thumbnail output DMA circuit428. Color processing stage312includes additional components not shown inFIG.4. Moreover, some components of color processing stage312described in relation toFIG.4may be embodied in various combinations of hardware, firmware, or software.

Image data402(e.g., in RGB color format) may be passed onto color processing stage312, e.g., from noise processing stage310. Image data402may be processed within color processing stage312to generate an input image403and a thumbnail image404(e.g., downscaled version of input image403). Pixels of thumbnail image404may be passed onto thumbnail image processing circuit406, as well as onto resizing circuit420. Pixels of input image403may be passed onto reintegration circuit416.

Thumbnail image processing circuit406may determine an illumination map410. Illumination map410may include a weight map (e.g., a weight map410A as described in relation toFIG.6), where each weight in the weight map represents an intensity level of a respective chrominance class (e.g., warm, cool, and neutral) for a respective source pixel in thumbnail image404. In addition to the weight map, illumination map410may also include information about a gain (e.g., gain410B as described in relation toFIG.6) for each global chrominance class (e.g., warm gain, and cool gain) for each source pixel in thumbnail image404. Thumbnail image processing circuit406may further apply a set of weights from the weight map for the source pixel to component values of color channels of the source pixel in thumbnail image404to generate component values of the color channels of a target pixel in a target thumbnail image408. Thumbnail image404, target thumbnail image408, and illumination map410may be passed onto resizing circuit420. When resizing circuit420is bypassed, thumbnail image404, target thumbnail image408, and illumination map410may be passed directly onto thumbnail output DMA circuit428.

Resizing circuit420may resize thumbnail image404, target thumbnail image408, and illumination map410(e.g., each to a size of input image403) and generate a resized version of thumbnail image422, a resized version of target thumbnail image424, and a resized version of illumination map426, respectively. Resized version of thumbnail image422, resized version of target thumbnail image424, and resized version of illumination map426may be stored in e.g., thumbnail output DMA circuit428. At least portions of resized version of thumbnail image422, resized version of target thumbnail image424, and resized version of illumination map426may be fetched from thumbnail output DMA circuit428as illumination data412and passed onto reintegration circuit416. Illumination data412may include, e.g., weight map410A (or its resized version) that is part of illumination map410(or part of resized illumination map426). In one or more embodiments where resizing circuit420is bypassed, resized version of thumbnail image422is thumbnail image404, resized version of target thumbnail image424is target thumbnail image408, and resized version of illumination map426is illumination map410.

Reintegration circuit416may apply weight map410A from illumination data412to a set of coefficients414(e.g., polynomial coefficients) to generate a weighted set of coefficients, and may apply the weighted set of coefficients to pixel values in input image403to generate an output image418. Output image418may be passed onto one or more additional components of color processing stage312for color processing of output image418to generate output image data432. Output image data432may be passed onto, e.g., output rescale module314and/or output interface316. More details about a structure and operation of thumbnail image processing circuit406are provided in relation toFIGS.6and7. More details about a structure and operation of reintegration circuit416are provided in relation toFIG.8. While operations of MIWB have been described with respect to thumbnail image404, in some embodiments, one or more of the operations of MWIB are performed on input image403or a processed version of input image403.

FIG.5illustrates input image403and corresponding thumbnail image404with areas of different color intensities (e.g., color temperatures) for a color channel, according to one embodiment. Input image403may be a full resolution image, and may include areas502A,504A,506A,508A,510A and512A of different color intensities, e.g., due to various shadows generated from different ambient light sources. For example, area502A may have a first (or lowest) color intensity; area504A may have a second color intensity higher than the first color intensity; area506A may have a third color intensity higher than the second color intensity; area508A may have a fourth color intensity higher than the third color intensity; area510A may have a fifth color intensity higher than the fourth color intensity; and area512A may have a sixth (or highest) color intensity higher than the fifth color intensity. Input image403may include fewer or more areas of different color intensities than what is illustrated inFIG.5. Each source pixel in input image403may be represented by a set of weights (e.g., cool, warm, and neutral weights), and each weight in the set of weights may represent an intensity level of a respective chrominance class (e.g., cool, warm, or neutral chrominance class) of multiple chrominance classes (e.g., cool, warm, and neutral chrominance classes) for the source pixel. For example, if a source pixel belongs to area512A of a high color intensity (e.g., high color temperature or cool chroma), then a corresponding cool weight for the source pixel would be high, and corresponding warm and neutral weights for the source pixel would be relatively small. In contrast, if the source pixel belongs to area502A in input image403of a low color intensity (e.g., low color temperature or warm chroma), then a corresponding warm weight for the source pixel would be the highest, and corresponding cool and neutral weights for the source pixel would be relatively small. The values of the cool, warm, and neutral weights may be normalized such that their sum is equal to a predetermined value (e.g., one).

Thumbnail image404may be generated by downscaling full resolution input image403along both horizontal and vertical directions. The size of thumbnail image404may be, e.g., 144×152 pixels. A ratio between the full resolution of input image403and a resolution of thumbnail image404can be integer or non-integer. In some embodiments, thumbnail image404is generated by downscaling input image403such that thumbnail image404includes areas502B,504B,506B,508B,510B and512B of different color intensities, and each area502B,504B,506B,508B,510B and512B represents a downscaled version of a respective area502A,504A,506A,508A,510A and512A in input image403. Similarly as for input image403, each source pixel in thumbnail image404may be represented by a set of weights (e.g., cool, warm, and neutral weights), and each weight in the set of weights may represent an intensity level of a respective chrominance class (e.g., cool, warm, or neutral chrominance class) of multiple chrominance classes (e.g., cool, warm, and neutral chrominance classes) for the source pixel. For example, if a source pixel belongs to an area in thumbnail image404of a relatively high color intensity (e.g., high color temperature or cool chroma), then a corresponding cool weight for the source pixel would be higher and corresponding warm and neutral weights for the source pixel would be smaller. In contrast, if the source pixel belongs to an area in thumbnail image404of a relatively low color intensity (e.g., low color temperature or warm chroma), then a corresponding warm weight for the source pixel would be higher and corresponding cool and neutral weights for the source pixel would be smaller.

Example Thumbnail Image Processing Circuit

FIG.6is a block diagram illustrating a detailed view of thumbnail image processing circuit406, according to one embodiment. Thumbnail image processing circuit406may perform color related processing of thumbnail image404obtained by, e.g., downscaling input image403. Thumbnail image processing circuit406may include a color space conversion circuit602, a spatial filtering circuit606coupled to an output of color space conversion circuit602, a chroma adjustment circuit610coupled to an output of spatial filtering circuit606, a chroma components calculation circuit614coupled to an output of chroma adjustment circuit610, a weights determination circuit618coupled to an output of chroma components calculation circuit614, a weight map calculation circuit622coupled to an output of weights determination circuit618, a spatial filtering circuit626coupled to an output of weight map calculation circuit622, a global weights calculation circuit628coupled to an output of color space conversion circuit602, a mixing circuit632coupled to an output of global weights calculation circuit628, a gain calculation circuit636(e.g., with white balance registers638) coupled to outputs of spatial filtering circuit626and mixing circuit632, a target thumbnail generator circuit640coupled to outputs of color space conversion circuit602and spatial filtering circuit626, and an illumination map generator circuit642coupled to outputs of spatial filtering circuit626and gain calculation circuit638. Thumbnail image processing circuit406may include more or fewer components than what is shown inFIG.6. Moreover, the various components of thumbnail image processing circuit406described in relation toFIG.6may be embodied in various combinations of hardware, firmware, or software.

Color space conversion circuit602may perform color space conversion of pixel values of thumbnail image404in a first color format (e.g., RGB color format) to generate a version of thumbnail image604in a second color format (e.g., YCbCr color format). Color space conversion circuit602may convert the pixel values of thumbnail image404in the first color format using multiple color space conversion matrices (e.g., an inverse 3×3 color space conversion matrix, and a pair of direct 3×3 color space conversion matrices) and clipping to obtain the pixel values of version of thumbnail image604in the second color format. The pixel values of version of thumbnail image604may be passed onto spatial filtering circuit606, as well as onto global weights calculation circuit628.

Spatial filtering circuit606may perform spatial filtering (e.g., spatial 3×3 low-pass filtering) of the pixel values of version of thumbnail image604to generate filtered pixel values of version of thumbnail image608. Alternatively, filtering operations performed by spatial filtering circuit606may be performed as part of color space conversion circuit602. Filtered pixel values of version of thumbnail image608in the second color format (e.g., YCbCr color format) may be passed onto chroma adjustment circuit610.

Chroma adjustment circuit610may adjust chroma component values of pixels in a version of thumbnail image608to generate a chroma-adjusted version of thumbnail image612. Chroma adjustment circuit610may adjust (e.g., scale) chroma component values of a source pixel in version of thumbnail image608to generate adjusted chroma component values of the source pixel in chroma-adjusted version of thumbnail image612. Chroma adjustment circuit610may adjust the chroma component values (e.g., Cr and Cb component values) of the source pixel version of thumbnail image608, e.g., by applying a configurable two channel chroma scaler and clipping to generate the adjusted chroma component values (e.g., adjusted Cr and Cb component values, Cr′ and Cb′) of the source pixel in chroma-adjusted version of thumbnail image612. Adjusted chroma component values of pixels in chroma-adjusted version of thumbnail image612(e.g., adjusted component values Cr′ and Cb′) may be passed onto chroma components calculation circuit614.

Chroma components calculation circuit614may determine chroma component values616of the source pixel using the adjusted chroma component values of the source pixel in a chroma-adjusted version of thumbnail image612. Chroma components calculation circuit614may determine a first of chroma component values616(e.g., C1 component value) of the source pixel by mapping the adjusted chroma component value Cr′ of the source pixel to C1 space using, e.g., look-up table (LUT) entries as part of chroma components calculation circuit614. Chroma components calculation circuit614may determine a second of chroma component values616(e.g., C2 component value) of the source pixel by applying, e.g., an offset to the adjusted chroma component value Cb′ of the source pixel. At this point, the conversion from the RGB color space to the YC1C2 color space is complete. Chroma component values616(e.g., C1 and C2 component values) for each source pixel in chroma-adjusted version of thumbnail image612may be passed onto weights determination circuit618. Additionally, a luma component value617(e.g., Y component value) for each source pixel in version of thumbnail image608may be extracted and also passed onto weights determination circuit618.

Weights determination circuit618may determine a set of initial weights620(e.g., an initial warm weight, an initial cool weight, and luma weight) for each source pixel using chroma component values616(e.g., C1 and C2 component values) and luma component value617(e.g., Y component value) for each source pixel. Each weight in set of initial weights620may represent an intensity level (e.g., normalized intensity level) of a respective chrominance class (e.g., warm, cool, or luma chrominance class) of multiple chrominance classes (e.g., warm, cool, and luma chrominance classes) for each source pixel.

FIG.7is a conceptual diagram illustrating determination of weights for a source pixel in YC1C2 color space, which may be implemented at weights determination circuit618, according to one embodiment. Weights determination circuit618may determine the initial cool weight (e.g., W1 weight) in set of initial weights620for the source pixel based on first chroma component value616(e.g., C1 component value) for the source pixel, configurable (e.g., software programmable) knee values702,704(e.g., CoolKnee1 and CoolKnee2), and configurable (e.g., software programmable) slope values706,708(e.g., both equal to C1WtSlope). Weights determination circuit618may determine the initial warm weight (e.g., W2 weight) in set of initial weights620for the source pixel based on first chroma component value616(e.g., C1 component value) for the source pixel, configurable (e.g., software programmable) knee values710,712(e.g., WarmKnee1 and WarmKnee2), and configurable (e.g., software programmable) slope values714,716(e.g., both equal to C1WtSlope). Weights determination circuit618may determine a third weight (e.g., W3 weight) in set of initial weights620for the source pixel based on second chroma component value616(e.g., C2 component value) for the source pixel and configurable (e.g., software programmable) knee values718,720(e.g., DstKnee1, −DstKnee2). Weights determination circuit618may finally determine the luma weight in set of initial weights620for the source pixel by comparing luma component value617for the source pixel with configurable (e.g., software programmable) luma intervals (e.g., eight luma intervals defined by nine luma values). Weights determination circuit618may pass set of initial weights620(e.g., the initial cool weight, the initial warm weight, the third weight and the luma weight) for each source pixel onto weight map calculation circuit622.

Weight map calculation circuit622may generate a weight map624for version of thumbnail image608based on set of initial weights620for each source pixel in version of thumbnail image608. Weight map624includes a two-dimensional array of set of weights (e.g., of a size corresponding to a size of version of thumbnail image608), and each weight in a corresponding set of weights of weight map624may represent one (normalized) intensity level of a respective chrominance class (e.g., cool, warm, or neutral chrominance class) for each source pixel in version of thumbnail image608. Weight map calculation circuit622may determine a warm weight (e.g., full warm weight) in weight map624for the source pixel by, e.g., multiplying the initial warm weight with the third weight and the luma weight in set of initial weights620for the source pixel. Weight map calculation circuit622may determine a cool weight (e.g., full cool weight) in weight map624for the source pixel by, e.g., multiplying the initial cool weight with the third weight and the luma weight in set of initial weights620for the source pixel. Weight map calculation circuit622may determine a neutral weight in weight map624for the source pixel such that a sum of the warm weight, the cool weight and neutral weight for the source pixel is equal to one (e.g., after normalization). Weight map calculation circuit622may pass weights of weight map624onto spatial filtering circuit626.

Spatial filtering circuit626may apply spatial filtering (e.g., 3×3 spatial filtering) to weights in weight map624to generate weight map410A (e.g., filtered multi-channel weight map). Spatial filtering circuit626may use the same filter coefficients for both horizontal and vertical filtering. Weight map410A may be passed as part of illumination map410and illumination data412onto reintegration circuit416(e.g., as shown inFIG.4andFIG.8). Weight map410A may be also passed onto global weights calculation circuit628, gain calculation circuit638and target thumbnail generator circuit640(e.g., as shown inFIG.6).

Global weights calculation circuit628may determine global weights630(e.g., global cool and warm weights or points) for the source pixel by applying a corresponding subset of weights (e.g., cool, warm, and neutral weights) for the source pixel in weight map410A to color component values of the source pixel in version of thumbnail image604. Each global weight630for the source pixel may represent an intensity level of a respective global chrominance class (e.g., coolness class, or warmness class) for the source pixel. Global weights calculation circuit628may first convert the color component values of the source pixel from YC1C2 color space to converted color component values of the source pixel in RGB color space. After that, global weights calculation circuit628may compute a first ratio of the converted color component values (e.g., ratio of R and G components) and a second ratio of the converted color component values (e.g., ratio of B and G components). Finally, global weights calculation circuit628may determine global weights630for the source pixel as weighted average values when the corresponding set of weights for the source pixel in weight map410A is applied to the first and second ratios. Global weights calculation circuit628may pass global weights630for each source pixel onto mixing circuit632.

Mixing circuit632may mix each global weight630for the source pixel with a corresponding mixing value (e.g., warm mixing value and cool mixing value) for the source pixel to generate a white gain634for each global chrominance class (e.g., warm white gain and cool white gain) for the source pixel. Mixing circuit632may pass white gains634(e.g., warm white gain and cool white gain) for each source pixel to gain calculation circuit636.

Gain calculation circuit636may determine a gain410B for each global chrominance class (e.g., warm gain, cool gain) for the source pixel by processing each weight gain634with a white balance value for the source pixel. White balance values may be stored in white balance registers638(e.g., as part of gain calculation circuit636, or alternatively as part of mixing circuit632). Gain calculation circuit636may further perform clipping to generate final values of gains410B (e.g., warm gain and cool gain) for the source pixel. In some embodiments, gain calculation circuit636can perform operations of mixing circuit632. Gain calculation circuit636may pass gains410B (e.g., warm gain and cool gain) for each source pixel onto illumination map generator circuit642.

Target thumbnail generator circuit640may apply the corresponding subset of weights (e.g., cool, warm, and neutral weights) for the source pixel in weight map410A to color component values (R, G and B component values) for the source pixel in version of thumbnail image604to generate color component values (R, G and B component values) of a target pixel in target thumbnail image408. Target thumbnail image408may feature corrected colors of shadows relative to original thumbnail image404. Target thumbnail image408may be resized (e.g., by resizing circuit420to full resolution of input image403) and stored in thumbnail output DMA circuit428(e.g., as shown inFIG.4).

Illumination map generator circuit642may generate illumination map410by merging each set of weight (e.g., cool, warm, and neutral weight) for each source pixel in weight map410A with gains410B (e.g., warm gain, and cool gain) for each source pixel. Illumination map410may thus include a two-dimensional array of multi-channel points, and each multi-channel point in illumination map410may represent, e.g., five channel point with information about weights of different chrominance classes (e.g., cool, warm, and neutral) and information about white gains of global chrominance classes (e.g., coolness and warmness) for each source pixel. Illumination map410may be resized (e.g., by resizing circuit420to full resolution of input image403) and stored in thumbnail output DMA circuit428(e.g., as shown inFIG.4). Alternatively, resizing circuit420may be bypassed and illumination map410may be stored in thumbnail output DMA circuit428without any resizing.

Example Reintegration Circuit

FIG.8is a block diagram illustrating a detailed view of reintegration circuit416, according to one embodiment. Reintegration circuit416may transfer a preferred look (e.g., “steal the feel”) from a thumbnail image (e.g., target thumbnail image408) to a full resolution image (e.g., output image418). Reintegration circuit416may receive illumination data412(e.g., from thumbnail output DMA circuit428) that include weight map410A. Reintegration circuit416may apply weight map410A from illumination data412to a set of coefficients414(e.g., polynomial coefficients) to generate a weighted set of coefficients, and apply the weighted set of coefficients to input image403to generate output image418. Output image418may have corrected colors of shadows relative to input image403. Reintegration circuit416may include an up-sampling circuit802, a weighted summation circuit806coupled to an output of up-sampling circuit802, and a coefficients application circuit810coupled to an output of weighted summation circuit806. Reintegration circuit416may include more or fewer components than what is shown inFIG.8. Moreover, the various components of reintegration circuit416described in relation toFIG.8may be embodied in various combinations of hardware, firmware, or software.

Up-sampling circuit802may perform up-sampling (e.g., grid-based bilinear up-sampling) of weight map410A from illumination data412to generate an up-sampled weight map804. Each weight for each color channel in up-sampled weight map804may be determined by applying, e.g., bilinear interpolation of weights for that color channel that correspond to a neighboring grid (e.g., 2×2 grid). Each weight in up-sampled weight map804may represent one intensity level of the respective chrominance class (e.g., warm, cool, or neutral chrominance class) for each color channel of a source pixel in input image403. Up-sampled weight map804may be passed onto weighted summation circuit806.

Weighted summation circuit806may apply up-sampled weight map804to set of coefficients414(e.g., polynomial coefficients) to generate a weighted set of coefficients808. Weighted summation circuit806may determine each weighted coefficient808for each color channel (e.g., of R, G, B color channels) by performing weighted summation of coefficients414for that color channel after a corresponding subset of weights in up-sampled weight map804are applied to coefficients414. Weighted set of coefficients808may be passed onto coefficients application circuit810.

Coefficients application circuit810may be a filtering circuit that applies weighted set of coefficients808to source pixel values in input image403to generate processed pixel values in output image418. Coefficients application circuit810may apply a weighted coefficient808for each color channel (e.g., each of R, G, B color channels) to a component value for each color channel of a source pixel in input image403to generate a processed component value for each color channel of a processed pixel in output image418.

Example Process of MIWB with Thumbnail Processing

FIG.9is a flowchart illustrating a method of multi-illumination white balance with thumbnail processing performed by an image processor (e.g., ISP206), according to one embodiment. The image processor determines902(e.g., via weights determination circuit618) a set of initial weights for a source pixel in a version of a thumbnail image by determining component values for color channels of the source pixel.

The image processor may adjust (e.g., via chroma adjustment circuit610) chroma component values of the source pixel to generate adjusted chroma component values of the source pixel. The image processor may determine (e.g., via chroma components calculation circuit614) a first chroma component value and a second chroma component value of the source pixel using the adjusted chroma component values. The image processor may determine (e.g., via weights determination circuit618) the set of initial weights for the source pixel using the first chroma component value and the second chroma component value.

The image processor may perform (e.g., via color space conversion circuit602) color space conversion of pixel values of the thumbnail image in a first color format to generate the version of the thumbnail image in a second color format. The image processor may perform (e.g., via spatial filtering circuit606) spatial filtering of pixel values of the thumbnail image to generate the version of the thumbnail image.

The image processor determines904(e.g., via weight map calculation circuit622) a set of weights for the source pixel in a weight map for the version of the thumbnail image, each weight in the set of weights determined based on corresponding initial weights from the set of initial weights, and each weight in the set of weights representing an intensity level of a respective chrominance class of multiple chrominance classes for the source pixel. The image processor may determine (e.g., via global weights calculation circuit628) global weights for the source pixel by applying the set of the weights to color component values of the source pixel, each of the global weights representing an intensity level of a respective global chrominance class of multiple global chrominance classes for the source pixel. The image processor may determine (e.g., via gain calculation circuit638) a gain for each global chrominance class of the global chrominance classes for the source pixel by processing the global weights for the source pixel and a corresponding white balance gain for the source pixel. The image processor may mix (e.g., via mixing circuit632) each of the global weights for the source pixel with a corresponding mixing value for the source pixel to generate a white gain for each global chrominance class of the global chrominance classes for the source pixel. The image processor may determine (e.g., via gain calculation circuit638) the gain for each global chrominance class of the global chrominance classes for the source pixel by clipping the white gain for each global chrominance class of the global chrominance classes for the source pixel.

The image processor may adjust (e.g., via chroma adjustment circuit610) chroma component values of pixels of the version of the thumbnail image to generate a chroma-adjusted version of the thumbnail image. The image processor may determine (e.g., via weights determination circuit618) the set of initial weights for each pixel in the chroma-scaled version of the thumbnail image using the adjusted chroma component values. The image processor may determine (e.g., via weight map calculation circuit622) the weight map by computing a set of full weights for each pixel in the chroma-scaled version of the thumbnail image using the set of initial weights for each pixel in the chroma-scaled version of the thumbnail image. The image processor may generate (e.g., via spatial filtering circuit626) a filtered multi-channel weight map by applying spatial filtering to the weight map, the filtered multi-channel weight map including a two-dimensional array of weights, each weight in the array representing an intensity level of the respective chrominance class for each pixel in the thumbnail image.

The image processor applies906(e.g., via target thumbnail generator circuit640) the set of weights to values of the color channels of the source pixel to generate color component values of the color channels of a target pixel in a target thumbnail image. The image processor may store (e.g., at thumbnail output DMA circuit428) least one of an illumination map, the target thumbnail image, and the thumbnail image, the illumination map including the weight map and the gain for each global chrominance class.

Embodiments of the process as described above with reference toFIG.9are merely illustrative. Moreover, sequence of the process may be modified or omitted.

While particular embodiments and applications have been illustrated and described, it is to be understood that the invention is not limited to the precise construction and components disclosed herein and that various modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed herein without departing from the spirit and scope of the present disclosure.