PATENT DOCUMENT

Publication Number: US-10547863-B1
Application Number: US-201816100772-A
Country: US
Kind Code: B1

Title: Image statistics for motion detection

Abstract:
Embodiments of the present disclosure relate to generating motion vectors. An image signal processor includes a statistics circuit and a vector correlation analysis circuit. The statistics circuit determines image statistics such as vectors representing sums of pixel values of rows or columns of blocks of an image. Additionally, the statistics circuit may mix or aggregate sums of multiple color components. The vector correlation analysis performs cross-correlation between vectors of a current image and reference vectors of a prior image to determine cross-correlation scores. The vector correlation analysis generates a motion vector by identifying shifts in horizontal and vertical directions corresponding to peak values of cross-correlation scores.

Claims:
What is claimed is: 
     
       1. An image signal processor comprising:
 a statistics circuit configured to:
 determine row sums of pixel values of each block of pixels in a current image, and 
 determine column sums of the pixel values of each block of pixels in the current image; and 
 
 a vector correlation analysis (VCA) circuit coupled to the statistics circuit, the VCA circuit configured to:
 determine first cross-correlation scores between the row sums of the pixel values of each block of pixels in the current image with row sums of pixel values of a corresponding block of pixels in a prior image preceding the current image, 
 determine second cross-correlation scores between the column sums of the pixel values of each block of pixels in the current image with column sums of the pixel values of a corresponding block of pixels in the prior image, and 
 generate a motion vector for each block of pixels in the current image by identifying a vertical shift corresponding to a greatest one of the first cross-correlation scores and a horizontal shift corresponding to a greatest one of the second cross-correlation scores. 
 
 
     
     
       2. The image signal processor of  claim 1 , wherein the statistics circuit comprises:
 a first summation circuit configured to determine the row sums by adding the pixel values in each row of each block; and 
 a second summation circuit configured to determine the column sums by adding the pixel values in each column of each block. 
 
     
     
       3. The image signal processor of  claim 2 , wherein the statistics circuit further comprises:
 a mixer coupled to the first summation circuit and the second summation circuit, the mixer configured to aggregate row sums and aggregate column sums of pixel values of a plurality of color components. 
 
     
     
       4. The image signal processor of  claim 3 , wherein the plurality of color components include components for a red, red subtype of green, blue, and blue subtype of green. 
     
     
       5. The image signal processor of  claim 3 , wherein the statistics circuit further comprises:
 a first multiplexer having (i) a first input coupled to output of the first summation circuit and (ii) a second input coupled to output of the second summation circuit, to selectively forward the row sums and the column sums to the mixer; and 
 a second multiplexer coupled to the first multiplexer and the mixer, the second multiplexer configured to select between outputs of the first multiplexer and the mixer for storage using direct memory access. 
 
     
     
       6. The image signal processor of  claim 1 , wherein a horizontal element of the motion vector is an amount of the horizontal shift and a vertical element of the motion vector is an amount of the vertical shift. 
     
     
       7. The image signal processor of  claim 1 , wherein the VCA circuit is further configured to determine cross-correlation scores by:
 determining an amount of horizontal shift for each of the blocks of pixels in the current image; and 
 determining an amount of vertical shift for each of the blocks of pixels in the current image. 
 
     
     
       8. The image signal processor of  claim 1 , wherein the VCA circuit is further configured to determine cross-correlation scores by:
 aggregating row sums of a first plurality of the blocks of pixels in the current image in a horizontal direction to obtain an aggregate row sum; 
 aggregating column sums of a second plurality of the blocks of pixels in the current image in a vertical direction to obtain an aggregate column sum; 
 determining an amount of vertical shift for the first plurality of the blocks of pixels using the aggregate row sum; and 
 determining an amount of horizontal shift for the second plurality of the blocks of pixels using the aggregate column sum. 
 
     
     
       9. The image signal processor of  claim 1 , wherein:
 the row sums of the pixel values of each block of pixels in the current image are determined by adding a first number of rows of pixel values greater than a second number of rows of pixel values added for the row sums of pixel values of the corresponding block of pixels in the prior image, and 
 the column sums of the pixel values of each block of pixels in the current image are determined by adding a third number of columns of pixel values greater than a fourth number of columns of pixel values added for the column sums of pixel values of the corresponding block of pixels in the prior image. 
 
     
     
       10. The image signal processor of  claim 1 , wherein the VCA circuit is further configured to process one or more of the row sums of pixel values or one or more of the column sums of pixel values using one or more of: cropping, spatial binning, normalization, or transformation using a lookup table. 
     
     
       11. A method comprising:
 determining row sums of pixel values of each block of pixels in a current image, and 
 determining column sums of the pixel values of each block of pixels in the current image; 
 determining first cross-correlation scores between the row sums of the pixel values of each block of pixels in the current image with row sums of pixel values of a corresponding block of pixels in a prior image preceding the current image; 
 determining second cross-correlation scores between the column sums of the pixel values of each block of pixels in the current image with column sums of the pixel values of a corresponding block of pixels in the prior image; and 
 generating a motion vector for each block of pixels in the current image by identifying a vertical shift corresponding to a greatest one of the first cross-correlation scores and a horizontal shift corresponding to a greatest one of the second cross-correlation scores. 
 
     
     
       12. The method of  claim 11 , further comprising:
 aggregating row sums and aggregate column sums of pixel values of a plurality of color components. 
 
     
     
       13. The method of  claim 11 , wherein determining the first and second cross-correlation scores comprises:
 determining an amount of vertical shift for each of the blocks of pixels in the current image; and 
 determining an amount of horizontal shift for each of the blocks of pixels in the current image. 
 
     
     
       14. The method of  claim 11 , wherein determining the first and second cross-correlation scores comprises:
 aggregating row sums of a first plurality of the blocks of pixels in the current image in a horizontal direction to obtain an aggregate row sum; 
 aggregating column sums of a second plurality of the blocks of pixels in the current image in a vertical direction to obtain an aggregate column sum; 
 determining an amount of vertical shift for the first plurality of the blocks of pixels using the aggregate row sum; and 
 determining an amount of horizontal shift for the second plurality of the blocks of pixels using the column row sum. 
 
     
     
       15. An electronic device comprising:
 an image sensor configured to capture a current image; and 
 an image signal processor coupled to the image sensor, the image signal processor comprising:
 a statistics circuit configured to:
 determine row sums of pixel values of each block of pixels in the current image 
 determine column sums of the pixel values of each block of pixels in the current image; and 
 
 a vector correlation analysis (VCA) circuit coupled to the statistics circuit, the VCA circuit configured to:
 determine first cross-correlation scores between the row sums of the pixel values of each block of pixels in the current image with row sums of pixel values of a corresponding block of pixels in a prior image preceding the current image, 
 determine second cross-correlation scores between the column sums of the pixel values of each block of pixels in the current image with column sums of the pixel values of a corresponding block of pixels in the prior image, and 
 generate a motion vector for each block of pixels in the current image by identifying a vertical shift corresponding to a greatest one of the first cross-correlation scores and a horizontal shift corresponding to a greatest one of the second cross-correlation scores. 
 
 
 
     
     
       16. The electronic device of  claim 15 , wherein the statistics circuit comprises:
 a first summation circuit configured to determine the row sums by adding the pixel values in each row of each block; 
 a second summation circuit configured to determine the column sums by adding the pixel values in each column of each block; and 
 a mixer coupled to the first summation circuit and the second summation circuit, the mixer configured to aggregate the row sums and aggregate the column sums of pixel values of a plurality of color components. 
 
     
     
       17. The electronic device of  claim 16 , wherein the plurality of color components include components for a red, red subtype of green, blue, and blue subtype of green. 
     
     
       18. The electronic device of  claim 15 , wherein a horizontal element of the motion vector is an amount of the horizontal shift and a vertical element of the motion vector is an amount of the vertical shift. 
     
     
       19. The electronic device of  claim 15 , wherein the VCA circuit is further configured to determine cross-correlation scores by:
 determining an amount of horizontal shift for each of the blocks of pixels in the current image; and 
 determining an amount of vertical shift for each of the blocks of pixels in the current image. 
 
     
     
       20. The electronic device of  claim 15 , wherein the VCA circuit is further configured to determine cross-correlation scores by:
 aggregating row sums of a first plurality of the blocks of pixels in the current image in a horizontal direction to obtain an aggregate row sum; 
 aggregating column sums of a second plurality of the blocks of pixels in the current image in a vertical direction to obtain an aggregate column sum; 
 determining an amount of vertical shift for the first plurality of the blocks of pixels using the aggregate row sum; and 
 determining an amount of horizontal shift for the second plurality of the blocks of pixels using the column row sum.

Description:
BACKGROUND 
     1. Field of the Disclosure 
     The present disclosure relates a circuit and methods for processing images and more specifically for autofocusing images using motion estimation. 
     2. Description of the Related Arts 
     Image data captured by an image sensor or received from other data sources is often processed in an image processing pipeline before further processing or consumption. For example, raw image data may be corrected, filtered, or otherwise modified before being provided to subsequent components such as a video encoder. To perform corrections or enhancements for captured image data, various components, unit stages or modules may be employed. 
     Such an image processing pipeline may be structured so that corrections or enhancements to the captured image data can be performed in an expedient way without consuming other system resources. Although many image processing algorithms may be performed by executing software programs on central processing unit (CPU), execution of such programs on the CPU would consume significant bandwidth of the CPU and other peripheral resources as well as increase power consumption. Hence, image processing pipelines are often implemented as a hardware component separate from the CPU and dedicated to perform one or more image processing algorithms. 
     SUMMARY 
     Embodiments relate to motion estimation and autofocusing of images. An image signal processor may determine statistics of pixels of an image to generate a motion vector. Pixel values of a current image may be compared with pixel values of a prior image to determine shift between the images. The motion vector may indicate information associated with a property of an image such as an amount of rotation or shift in a horizontal direction and a vertical direction. 
     In one embodiment, the motion vector can be used to assist autofocusing of an image. The motion vector may be determined by accumulating pixel values of a motion detection window of an image. The image may also include one or more autofocus windows. If it is determined that a given one of the autofocus windows follows the motion detection window by at least a threshold vertical distance, at least one property of the autofocus window may be adjusted according to at least the current motion vector. In some embodiments, adjusting the at least one property of the autofocus window includes shifting a horizontal or vertical location of the autofocus window to compensate for detected motion in the motion detection window. 
     In some embodiments, a vertical directional shift and horizontal directional shift is identified using cross-correlation scores of pixel values of the current and prior images, which are processed by a vector correlation analysis circuit. A statistics circuit may include summation circuits for adding pixel values in rows or columns of the current image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a high-level diagram of an electronic device, according to one embodiment 
         FIG. 2  is a block diagram illustrating components in the electronic device, according to one embodiment. 
         FIG. 3  is a block diagram illustrating image processing pipelines implemented using an image signal processor, according to one embodiment. 
         FIG. 4  is a block diagram illustrating a motion estimator, according to one embodiment. 
         FIG. 5  is a block diagram illustrating a pipeline of the statistics circuit, according to one embodiment. 
         FIG. 6  is a diagram of row sums of blocks of an image, according to one embodiment. 
         FIG. 7  is a diagram of column sums of blocks of an image, according to one embodiment. 
         FIG. 8  is a diagram of aggregated sums of blocks of an image, according to one embodiment. 
         FIG. 9  is a diagram of autofocus windows, according to one embodiment. 
         FIG. 10  is a flowchart illustrating a method of generating a motion vector, according to one embodiment. 
         FIG. 11  is a flowchart illustrating a method of performing autofocusing, according to one embodiment. 
     
    
    
     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 autofocusing of images using motion vectors generated by an image signal processor of a device. An image being processed may include one or more motion detection windows associated with a motion vector as well as one or more autofocus windows. An autofocus window that follows a motion detection window by at least a threshold vertical distance may be selected, e.g., to account for a period of time (or latency) for determining a motion vector of the motion detection window. The device may perform autofocusing by shifting location of the selected autofocus window. 
     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, Calif. Other portable electronic devices, such as wearables, laptops or tablet computers, are optionally used. In some embodiments, the device is not a portable communications 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 touch pad). An example electronic device described below in conjunction with  FIG. 1  (e.g., device  100 ) 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. 
     Figure ( FIG. 1  is a high-level diagram of an electronic device  100 , according to one embodiment. Device  100  may include one or more physical buttons, such as a “home” or menu button  104 . Menu button  104  is, for example, used to navigate to any application in a set of applications that are executed on device  100 . In some embodiments, menu button  104  includes a fingerprint sensor that identifies a fingerprint on menu button  104 . The fingerprint sensor may be used to determine whether a finger on menu button  104  has a fingerprint that matches a fingerprint stored for unlocking device  100 . Alternatively, in some embodiments, menu button  104  is implemented as a soft key in a graphical user interface (GUI) displayed on a touch screen. 
     In some embodiments, device  100  includes touch screen  150 , menu button  104 , push button  106  for powering the device on/off and locking the device, volume adjustment buttons  108 , Subscriber Identity Module (SIM) card slot  110 , head set jack  112 , and docking/charging external port  124 . Push button  106  may 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, device  100  also accepts verbal input for activation or deactivation of some functions through microphone  113 . The device  100  includes 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, speaker  111 , microphone  113 , input/output (I/O) subsystem, and other input or control devices. Device  100  may include one or more image sensors  164 , one or more proximity sensors  166 , and one or more accelerometers  168 . The device  100  may include components not shown in  FIG. 1 . 
     Device  100  is only one example of an electronic device, and device  100  may have more or fewer components than listed above, some of which may be combined into a components or have a different configuration or arrangement. The various components of device  100  listed above are embodied in hardware, software, firmware or a combination thereof, including one or more signal processing and/or application specific integrated circuits (ASICs). 
       FIG. 2  is a block diagram illustrating components in device  100 , according to one embodiment. Device  100  may perform various operations including image processing. For this and other purposes, the device  100  may include, among other components, image sensor  202 , system-on-a chip (SOC) component  204 , system memory  230 , persistent storage (e.g., flash memory)  228 , orientation sensor  234 , and display  216 . The components as illustrated in  FIG. 2  are merely illustrative. For example, device  100  may include other components (such as speaker or microphone) that are not illustrated in  FIG. 2 . Further, some components (such as orientation sensor  234 ) may be omitted from device  100 . 
     Image sensor  202  is a component for capturing image data and may be embodied, for example, as a complementary metal-oxide-semiconductor (CMOS) active-pixel sensor) a camera, video camera, or other devices. Image sensor  202  generates raw image data that is sent to SOC component  204  for further processing. In some embodiments, the image data processed by SOC component  204  is displayed on display  216 , stored in system memory  230 , persistent storage  228  or sent to a remote computing device via network connection. The raw image data generated by image sensor  202  may be in a Bayer color filter array (CFA) pattern (hereinafter also referred to as “Bayer pattern”). 
     Motion sensor  234  is a component or a set of components for sensing motion of device  100 . Motion sensor  234  may generate sensor signals indicative of orientation and/or acceleration of device  100 . The sensor signals are sent to SOC component  204  for various operations such as turning on device  100  or rotating images displayed on display  216 . 
     Display  216  is a component for displaying images as generated by SOC component  204 . Display  216  may include, for example, liquid crystal display (LCD) device or an organic light emitting diode (OLED) device. Based on data received from SOC component  204 , display  116  may display various images, such as menus, selected operating parameters, images captured by image sensor  202  and processed by SOC component  204 , and/or other information received from a user interface of device  100  (not shown). 
     System memory  230  is a component for storing instructions for execution by SOC component  204  and for storing data processed by SOC component  204 . System memory  230  may 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 memory  230  may store pixel data or other image data or statistics in various formats. 
     Persistent storage  228  is a component for storing data in a non-volatile manner. Persistent storage  228  retains data even when power is not available. Persistent storage  228  may be embodied as read-only memory (ROM), flash memory or other non-volatile random access memory devices. 
     SOC component  204  is embodied as one or more integrated circuit (IC) chip and performs various data processing processes. SOC component  204  may include, among other subcomponents, image signal processor (ISP)  206 , a central processor unit (CPU)  208 , a network interface  210 , sensor interface  212 , display controller  214 , graphics processor (GPU)  220 , memory controller  222 , video encoder  224 , storage controller  226 , and various other input/output (I/O) interfaces  218 , and bus  232  connecting these subcomponents. SOC component  204  may include more or fewer subcomponents than those shown in  FIG. 2 . 
     ISP  206  is hardware that performs various stages of an image processing pipeline. In some embodiments, ISP  206  may receive raw image data from image sensor  202 , and process the raw image data into a form that is usable by other subcomponents of SOC component  204  or components of device  100 . ISP  206  may 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 to  FIG. 3 . 
     CPU  208  may be embodied using any suitable instruction set architecture, and may be configured to execute instructions defined in that instruction set architecture. CPU  208  may be general-purpose or embedded processors using any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC, RISC, ARM or MIPS ISAs, or any other suitable ISA. Although a single CPU is illustrated in  FIG. 2 , SOC component  204  may include multiple CPUs. In multiprocessor systems, each of the CPUs may commonly, but not necessarily, implement the same ISA. 
     Graphics processing unit (GPU)  220  is graphics processing circuitry for performing graphical data. For example, GPU  220  may render objects to be displayed into a frame buffer (e.g., one that includes pixel data for an entire frame). GPU  220  may 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 interfaces  218  are hardware, software, firmware or combinations thereof for interfacing with various input/output components in device  100 . I/O components may include devices such as keypads, buttons, audio devices, and sensors such as a global positioning system. I/O interfaces  218  process data for sending data to such I/O components or process data received from such I/O components. 
     Network interface  210  is a subcomponent that enables data to be exchanged between devices  100  and 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 interface  210  and be stored in system memory  230  for subsequent processing (e.g., via a back-end interface to image signal processor  206 , such as discussed below in  FIG. 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 interface  210  may undergo image processing processes by ISP  206 . 
     Sensor interface  212  is circuitry for interfacing with motion sensor  234 . Sensor interface  212  receives sensor information from motion sensor  234  and processes the sensor information to determine the orientation or movement of the device  100 . 
     Display controller  214  is circuitry for sending image data to be displayed on display  216 . Display controller  214  receives the image data from ISP  206 , CPU  208 , graphic processor or system memory  230  and processes the image data into a format suitable for display on display  216 . 
     Memory controller  222  is circuitry for communicating with system memory  230 . Memory controller  222  may read data from system memory  230  for processing by ISP  206 , CPU  208 , GPU  220  or other subcomponents of SOC component  204 . Memory controller  222  may also write data to system memory  230  received from various subcomponents of SOC component  204 . 
     Video encoder  224  is hardware, software, firmware or a combination thereof for encoding video data into a format suitable for storing in persistent storage  128  or for passing the data to network interface w 10  for transmission over a network to another device. 
     In some embodiments, one or more subcomponents of SOC component  204  or some functionality of these subcomponents may be performed by software components executed on ISP  206 , CPU  208  or GPU  220 . Such software components may be stored in system memory  230 , persistent storage  228  or another device communicating with device  100  via network interface  210 . 
     Image data or video data may flow through various data paths within SOC component  204 . In one example, raw image data may be generated from the image sensor  202  and processed by ISP  206 , and then sent to system memory  230  via bus  232  and memory controller  222 . After the image data is stored in system memory  230 , it may be accessed by video encoder  224  for encoding or by display  116  for displaying via bus  232 . 
     In another example, image data is received from sources other than the image sensor  202 . For example, video data may be streamed, downloaded, or otherwise communicated to the SOC component  204  via wired or wireless network. The image data may be received via network interface  210  and written to system memory  230  via memory controller  222 . The image data may then be obtained by ISP  206  from system memory  230  and processed through one or more image processing pipeline stages, as described below in detail with reference to  FIG. 3 . The image data may then be returned to system memory  230  or be sent to video encoder  224 , display controller  214  (for display on display  216 ), or storage controller  226  for storage at persistent storage  228 . 
     Example Image Signal Processing Pipelines 
       FIG. 3  is a block diagram illustrating image processing pipelines implemented using ISP  206 , according to one embodiment. In the embodiment of  FIG. 3 , ISP  206  is coupled to image sensor  202  to receive raw image data. ISP  206  implements an image processing pipeline which may include a set of stages that process image information from creation, capture or receipt to output. ISP  206  may include, among other components, sensor interface  302 , central control module  320 , front-end pipeline stages  330 , back-end pipeline stages  340 , image statistics module  304 , vision module  322 , back-end interface  342 , and output interface  316 . ISP  206  may include other components not illustrated in  FIG. 3  or may omit one or more components illustrated in  FIG. 3 . 
     Sensor interface  302  receives raw image data from image sensor  202  and processes the raw image data into an image data processable by other stages in the pipeline. Sensor interface  302  may perform various preprocessing operations, such as image cropping, binning or scaling to reduce image data size. In some embodiments, pixels are sent from the image sensor  202  to sensor interface  302  in raster order (i.e., 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 and a single sensor interface  302  are illustrated in  FIG. 3 , when more than one image sensor is provided in device  100 , a corresponding number of sensor interfaces may be provided in ISP  206  to process raw image data from each image sensor. 
     Front-end pipeline stages  330  process image data in raw or full-color domains. Front-end pipeline stages  330  may include, but are not limited to, raw processing stage  306  and resample processing stage  308 . A raw image data may be in Bayer raw format, for example. In 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 a Bayer pattern. Raw processing stage  306  may process image data in a Bayer raw format. 
     The operations performed by raw processing stage  306  include, 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, and highlight recovery. 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 Bayer format). Components of ISP  206  may convert raw image data into image data in full-color domain, and thus, raw processing stage  306  may process image data in the full-color domain in addition to or instead of raw image data. 
     Resample processing stage  308  performs various operations to convert, resample, or scale image data received from raw processing stage  306 . Operations performed by resample processing stage  308  may 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 a 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 stage  308  converts RGB format into YCbCr format for further processing. 
     Central control module  320  may control and coordinate overall operation of other components in ISP  206 . Central control module  320  performs 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 ISP  206 , and interfacing with sensor interface  302  to control the starting and stopping of other components of ISP  206 . For example, central control module  320  may update programmable parameters for other components in ISP  206  while the other components are in an idle state. After updating the programmable parameters, central control module  320  may place these components of ISP  206  into a run state to perform one or more operations or tasks. Central control module  320  may also instruct other components of ISP  206  to store image data (e.g., by writing to system memory  230  in  FIG. 2 ) before, during, or after resample processing stage  308 . 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 stage  308  through backend pipeline stages  340 . 
     Image statistics module  304  performs 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  3 A statistics (Auto white balance (AWB), auto exposure (AE), autofocus (AF)), histograms (e.g., 2D color or component) and any other image data information may be collected or tracked. In some embodiments, certain pixels&#39; values, or areas of pixel values may be excluded from collections of certain statistics data (e.g., AF statistics) when preceding operations identify clipped pixels. The image statistics module  304  includes a motion estimator  305 , which may generate image statistics for autofocusing of images (e.g., performed in software and/or hardware). The motion estimator  305  is further described below with reference to  FIG. 4 . Although only a single statistics module  304  is illustrated in  FIG. 3 , multiple image statistics modules may be included in ISP  206 . In such embodiments, each statistic module may be programmed by central control module  320  to collect different information for the same or different image data. 
     Vision module  322  performs various operations to facilitate computer vision operations at CPU  208  such as facial detection in image data. The vision module  322  may 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 interface  342  receives image data from other image sources than image sensor  202  and forwards it to other components of ISP  206  for processing. For example, image data may be received over a network connection and be stored in system memory  230 . Back-end interface  342  retrieves the image data stored in system memory  230  and provides it to back-end pipeline stages  340  for processing. One of many operations that are performed by back-end interface  342  is converting the retrieved image data to a format that can be utilized by back-end processing stages  340 . For instance, back-end interface  342  may convert RGB, YCbCr 4:2:0, or YCbCr 4:2:2 formatted image data into YCbCr 4:4:4 color format. 
     Back-end pipeline stages  340  processes 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 stages  340  may convert image data to a particular full-color format before further processing. Back-end pipeline stages  340  may include, among other stages, noise processing stage  310  and color processing stage  312 . Back-end pipeline stages  340  may include other stages not illustrated in  FIG. 3 . 
     Noise processing stage  310  performs various operations to reduce noise in the image data. The operations performed by noise processing stage  310  include, 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 (i.e. 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 stage  312  may perform various operations associated with adjusting color information in the image data. The operations performed in color processing stage  312  include, 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 the central control module  320 ) may be bi-linearly 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 stage  312  to perform other special image effects, including black and white conversion, sepia tone conversion, negative conversion, or solarize conversion. 
     Output rescale module  314  may resample, transform and correct distortion on the fly as the ISP  206  processes image data. Output rescale module  314  may 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 module  314  may apply transforms to image data as it is processed at output rescale module  314 . Output rescale module  314  may 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 ISP  206  may 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 module  314  may 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, output rescale module  314  may implement a table of piecewise perspective transforms encoded as digital difference analyzer (DDA) steppers to perform a per-pixel perspective transformation between a 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 interface  316  to various other components of device  100 , as discussed above with regard to  FIGS. 1 and 2 . 
     In various embodiments, the functionally of components  302  through  342  may be performed in a different order than the order implied by the order of these functional units in the image processing pipeline illustrated in  FIG. 3 , or may be performed by different functional components than those illustrated in  FIG. 3 . Moreover, the various components as described in  FIG. 3  may be embodied in various combinations of hardware, firmware or software. 
     Example Motion Estimator 
       FIG. 4  is a block diagram illustrating a motion estimator  305 , according to one embodiment. The motion estimator  305  processes images to determine statistics such as shift between images in a horizontal or vertical direction. Additionally, the motion estimator  305  may use the statistics to generate motion vectors, for example, to be used for autofocusing. The motion estimator  305  may include, among other components, statistics circuit  402  and vector correlation analysis (VCA) circuit  408 . 
     In the embodiment of  FIG. 4 , statistics circuit  402  receives input image data  404  captured by the image sensor  202 . The input image data  404  may be provided by the sensor interface  302  or received from a source memory (e.g., system memory  230 , persistent storage  228 , or a cache) of the device  100 . The input image data  404  may have one or multiple color components or channels. In some embodiments, the image sensor  202  captures images using a Bayer filter including color filters for red, green, and blue. The input image data  404  may include color components for a red, red subtype of green (“Gr”), blue, and blue subtype of green (“Gb”). The color components may be arranged in any suitable order (e.g., GRBG, RGGB, BGGR, GBRG, etc.). In addition, the statistics circuit  402  can divide (e.g., a window of) the input image data  404  into blocks of pixels in the vertical and horizontal directions, e.g., where the blocks are adjacent to each other and/or do not overlap each other. In some embodiments, dimensions of the blocks may be even integer numbers, and the dimensions of the blocks may be at least four pixels. 
     The statistics circuit  402  generates image statistics such as row sums and column sums of pixel values (e.g., intensity values) of the input image data  404 . A row sum represents a sum of pixel values across a row of pixels in one or more blocks of an image. A column sum represents a sum of pixel values across a column of pixels in one or more blocks of an image. In some embodiments, the output of the statistics circuit  402  can be used to detect fixed pattern noise in images. The statistics circuit  402  may determine the sums by accumulating pixel values across a row or column of pixel values for each block of the input image data  404 . The pixel values may be accumulated for specific color components, and the statistics circuit  402  can apply weighted sums of multiple color components. 
     The statistics circuit  402  provides generated image statistics  406  to the VCA circuit  408  to perform further image processing. The image statistics  406  provided to the VCA circuit  408  may include statistics that are weighted summation of multiple color components. The statistics circuit  402  can store image statistics  410  or other relevant information to the system memory  230  via direct memory access. The image statistics  410  stored to system memory  230  may be for a particular color component, for example, so that image statistics for red, green, and blue are stored separately instead of being summed to a single component. Since direct memory access operates independently from the CPU  208 , the motion estimator  305  may offload resource intensive operations or other overhead associated with the motion estimation or autofocusing operations from the CPU  208 . The statistics circuit  402  is further described below with respect to  FIG. 5 . 
     The VCA circuit  408  generates a motion vector using the image statistics  406  received from the statistics circuit  402 . The motion vector indicates estimated motion of a current image relative to a prior image upon which the prior image statistics is based. As an example, the current and prior images each capture an image of an entity such as a person or an object. There may be shifting of the entity (and/or of the device  100 ) or movements of objects in images during a period of time between capturing of the current and prior images. The device  100  can use the motion vector to compensate for the estimated motion, which may improve quality or other attributes of the current image. In some embodiments, the VCA circuit  408  can enable detection for one of the horizontal or vertical directions and disable correction for the other direction, or enable detection for both directions. 
     The VCA circuit  408  generates motion vectors using cross-correlation scores. The VCA circuit  408  determines cross-correlation scores by cross-correlating sums of pixel values of a current image and those of a prior image. The sums of pixel values of the current image may be referred to herein as vectors, and the sums of pixel values of the prior image may be referred to herein as reference vectors. Sums of rows of pixel values represent a vertical directional vector, and cross-correlation scores between vertical directional vectors of current image and a prior image represents a vertical directional shift. Likewise, sums of columns of pixel values represent a horizontal directional vector, and cross-correlation scores between horizontal directional vectors of current image and a prior image represents a horizontal directional shift. In some embodiments, the VCA circuit  408  may implement normalized cross-correlation (NCC). 
     The VCA circuit  408  may retrieve vectors of current images from the image statistics  406 . Further, the VCA circuit  408  may retrieve reference vectors  412  from system memory  230  via direct memory access (DMA) or from a register. The reference vectors  412  may be previously generated by the statistics circuit  402  and may be modified in front end processing. For instance, the VCA circuit  408  performs one or more of (in any particular order): compressing data size of vectors, cropping vectors (e.g., shorten vectors to a target length), performing spatial binning, determining a weighted sum of multiple color components, applying offset and scaling factors to vector values, or performing gamma correction or non-linear transformation. In some embodiments, the VCA circuit  408  uses a look up table (LUT) for transforming values of a reference vector to reduce the impact of noise, boost responsivity of dark areas, or equalize a signal-to-noise ratio. In addition, the VCA circuit  408  may perform normalization to compensate for changes in lighting, e.g., exposure or white balance. The VCA circuit  408  may use spatial binning to reduce vector size and thus reduce processing time. A vector of a current image may also be modified in front end processing using one or more of the above mentioned techniques. 
     The VCA circuit  408  may provide the motion vector  414  to other components of the ISP  206  or SOC component  204  for further processing. For example, the CPU  208  may use the motion vector  414  to perform autofocusing operations, which is further described below with reference to  FIG. 9 . 
     Example Statistics Circuit 
       FIG. 5  is a block diagram illustrating a pipeline of the statistics circuit  402 , according to one embodiment. The statistics circuit  402  may include, among other components, row summation circuit  502 , row sum buffer  504 , column summation circuit  508 , column sum buffer  510 , first multiplexer  516 , mixer  520 , and second multiplexer  526 . 
     The row summation circuit  502  and the column summation circuit  508  receive image data  404  input to the statistics circuit  402 . The row summation circuit  502  determines row sums of an image, for example, by adding pixel values in the same row position across columns of blocks of an image, as described below with reference to  FIG. 6 . The row sum buffer  504  receives and stores values  506  of row sums or intermediate row sums. In particular, the row sum buffer  504  buffers the values as the row summation circuit  502  iterates across rows of the blocks to accumulate pixel values. 
     The column summation circuit  508  determines column sums of the image, for example, by adding pixel values in the same column position across rows of blocks of the image, as described below with reference to  FIG. 7 . The column sum buffer  510  receives and stores values  512  of column sums or intermediate column sums. In particular, the column sum buffer  510  buffers the values as the column summation circuit  508  iterates across columns of the blocks to accumulate pixel values. In various embodiments, the column summation circuit  508  and row summation circuit  502  determine sums of pixel values separately for different color components. 
     The first multiplexer  516  has an input coupled to the row sum buffer  504  and another input coupled to the column sum buffer  510  to receive accumulated sums  514  of pixel values from the buffers. Particularly, the first multiplexer  516  receives row sums and column sums from the row sum buffer  504  and the column sum buffer  510 , respectively. The first multiplexer  516  selectively forwards the row sums and the column sums to the mixer  520  or the second multiplexer  526 . 
     The mixer  520  is coupled to the row summation circuit  502  and the column summation circuit  508 , e.g., through the first multiplexer  516 . Accumulated sums received by the mixer  520  from the summation circuits may be associated with one given color component. The mixer  520  determines a weighted sum of (or “mixes”) row sums of pixel values and column sums of pixel values of multiple color components. The mixer  520  can output the sums as image statistics  406  and can also provide the sums to the second multiplexer  526 . The second multiplexer  526  selects between outputs of the first multiplexer  516  and the mixer  520  for storage using DMA. For instance, the second multiplexer  526  stores image statistics  410  for separate color components or a weighted sum of color components to system memory  230  or to registers. 
     In some embodiments, the first multiplexer  516  or the second multiplexer  526  may select outputs as indicated by parameter values retrieved from one or more registers. A parameter value may indicate that output to the VCA circuit  408  is enabled (e.g., an enable bit or flag). Additionally, a parameter value may indicate information for operation of the statistics circuit  402  or the VCA circuit  408 , for instance, a number of windows to be processed for an image, a number of blocks, a number of columns or rows, size of vectors, or color component configuration of an image (e.g., weights of R, Gr, B, and Gb for the mixer  520 ). 
     Example Sums of Pixel Values of Blocks 
       FIGS. 6 through 8  illustrate accumulation of sums of pixel values of a window (e.g., a motion detection window) of an image  902 . In the examples illustrated in  FIGS. 6 through 8 , a window of a current image is divided into a row of four blocks that are identical in dimension. Windows may be a subset of pixels of an image and may be rectangular-shaped. Since the blocks have identical dimensions, the blocks have a same number of rows and columns of pixels. In other embodiments, the motion estimator  305  may divide windows into any number of blocks arranged in any number of rows or columns. 
     In a sub-window mode, the statistics circuit  402  accumulates column sums and row sums for each block. Additionally, the VCA circuit  408  determines cross-correlation scores for each column sum and row sum of a block of the window with those from a prior image. In some embodiments, the VCA circuit  408  generates a motion vector for the window of the current image by determining a greatest one of the cross-correlation scores. For example, the greatest correlation score corresponds to the best estimation of vertical shift or horizontal shift detected by the motion estimator  305  across the blocks in the window. A window used for generating motion vectors may be referred to herein as a motion detection window. The VCA circuit  408  may generate other motion vectors having different values for other windows of a same image. For instance, a motion vector of a first window capturing an image of a moving object will result in greater amounts of shift than another motion vector of a second window capturing an image of a stationary object. The VCA circuit  408  may write motion vectors or associated values to one or more registers. 
       FIG. 6  is a diagram of row sums of blocks of an image, according to one embodiment. In the illustrated example, the statistics circuit  402  determines row sums  604 A,  604 B,  604 C, and  604 D for block  602 A,  602 B,  602 C, and  602 D, respectively. The VCA circuit  408  performs cross-correlation in a vertical correlation direction using vectors of the row sums  604 A-D and reference vectors of a prior image. In particular, the VCA circuit  408  correlates each of the vectors with a reference vector determined by adding pixel values of a corresponding segment in the prior image. For example, vector  604 A represents a sum of pixel values in the n th  column position (iterated over rows of the current image) of the first block of the illustrated window of the current image. Additionally, vector  606  represents a sum of pixel values in the n th  column position (iterated over rows of the prior image) of the first block of the same window of the prior image. 
     Since the size of the vector  604 A may be greater than the size of reference vector  606 , the VCA circuit  408  may determine whether any pixel values of the vector  604 A shifted in the vertical correlation direction relative to pixel values of the reference vector  606 . As output of the correlation of vector  604 A and reference vector  606 , the VCA circuit  408  determines cross-correlation score  608 , which corresponds to the correlation score at each vertical directional shift of pixels in the window between the current and prior image at the n th  column of the first block, with the greatest score value representing the best estimated vertical directional shift at the n th  column between the current and prior image. 
       FIG. 7  is a diagram of column sums of blocks of an image, according to one embodiment. In the illustrated example, the statistics circuit  402  determines column sums  704 A,  704 B,  704 C, and  704 D for block  702 A,  702 B,  702 C, and  702 D, respectively. The VCA circuit  408  performs cross-correlation in a horizontal correlation direction using vectors of the column sums  704 A-D and reference vectors of a prior image. Cross-correlation of column sums is substantially the same as cross-correlation of row sums, except for the different correlation direction. For instance, the VCA circuit  408  correlates each of the vectors with a reference vector determined by adding pixel values of a corresponding segment (e.g., column) in the prior image. Moreover, the VCA circuit  408  may determine whether any pixel values of the vector  704 A shifted in the horizontal correlation direction relative to pixel values of the reference vector  706  because the size of the vector  704 A may be greater than the size of reference vector  706 . 
     In various embodiments, the VCA circuit  408  uses peak finding to determine a greatest one of the cross-correlation scores in each of the correlation directions, e.g., horizontal and vertical. If it is determined that a window has multiple maximum cross-correlation scores, the VCA circuit  408  may select the first instance of a greatest score, e.g., corresponding to a vector closest to an origin of a coordinate system of the window. The VCA circuit  408  may also optionally perform sub-pixel location interpolation to obtain sub-pixel precision of the greatest scores. In sub-window mode, the VCA circuit  408  may determine average values or median values of multiple motion vectors of the blocks for a horizontal and vertical component of an overall motion vector of a window. In some embodiments, the average values may be rounded to the nearest integer. 
     The VCA circuit  408  may use less computational resources to calculate motion vectors using the embodiments described herein, in comparison to conventional motion detection methods. Convention methods can require comparison of a greater number of pixels between a current image and reference image by scanning across all pixels of the images, which can be time-consuming and introduce more latency. By dividing images into blocks for determining image statistics, the motion estimator  305  can reduce a number of required pixel value calculations or comparisons. Based on the locations and sizes of the windows, VCA circuit  408  may complete the motion vectors computation of windows ahead of the last row of the image frame. 
       FIG. 8  is a diagram of aggregated sums of blocks of an image, according to one embodiment. In aggregation mode, the statistics circuit  402  accumulates column sums and row sums for each block of a window. Further, the statistics circuit  402  aggregates the row sums of the blocks for the window. In the example shown in  FIG. 8 , the statistics circuit  402  aggregates row sums  804 A,  804 B,  804 C, and  804 D to generate vector  806  representing an aggregated row sum of a window. The statistics circuit  402  also aggregates the column sums of the blocks for the window. For instance, the statistics circuit  402  aggregates column sums  808 A,  808 B,  808 C, and  808 D to generate vector  810  representing an aggregated column sum of a window. 
     For a given window, the VCA circuit  408  determines a first cross-correlation score  808  in the vertical correlation direction and a second cross-correlation score  814  in the horizontal correlation direction. The VCA circuit  408  determines the cross-correlation score  808  by cross-correlating vector  806  and reference vector  816 , which is determined using an aggregate row sum in the prior image. The VCA circuit  408  determines the cross-correlation score  814  by cross-correlating vector  810  and reference vector  812 , which is determined using an aggregate column sum in the prior image. The VCA circuit  408  generates a motion vector for the window of the current image using the pair of cross-correlation scores  814  and  808 . Particularly, the cross-correlation score  814  represents amount of horizontal shift and the cross-correlation score  808  represents amount of vertical shift detected by the motion estimator  305  across the blocks in the window. 
     The VCA circuit  408  may determine whether to use sub-window mode or aggregation mode case-by-case based on characteristics of a given image. For example, sub-window mode is used to estimate local motion at the sub-window (or window) locations, while aggregation mode is used to estimate global motion. Relative to a motion vector in sub-window mode, a motion vector in aggregation mode covers a larger field (e.g., number of rows or columns of pixels), and thus may include additional image features. In embodiments where one or more images show a moving object, the VCA circuit  408  may determine to use sub-window mode to target motion estimation on the moving object. In other embodiments, the VCA circuit  408  may determine to use aggregation mode to estimate global motion of the camera that captured processed images. 
     Example Autofocusing 
       FIG. 9  is a diagram of autofocus windows, according to one embodiment. In the illustrated example, the statistics circuit  402  generates image statistics of motion detection window  904  of image  902 . Additionally, the VCA circuit  408  uses the image statistics to generate a motion vector of the motion detection window  904 , e.g., using the processes described above with respect to  FIGS. 6 through 8 . 
     In various embodiments, the motion estimator  305  processes pixels in a (e.g., raster) left-to-right and top-to-bottom manner, e.g., when accumulating sums of pixel values of rows or columns. An amount of time is required for the motion estimator  305  to execute steps for determining motion vectors of windows in images. The threshold vertical location (or coordinate)  910  shown in  FIG. 9  indicates the amount of time required to determine the motion vector of the motion detection window  904 . The amount of time includes  906 , which is the time to determine the motion vectors based on window  904 , of the statistics circuit  402  and/or the VCA circuit  408 . The amount of time may be based on coordinate position or size of the motion detection window  904 . For instance, due to the top-to-bottom processing order, windows toward the bottom of the image  902  will require a greater amount of time, relative to windows toward the top of the image  902 . The motion estimator  305  may account for a threshold vertical shift  908  as part of the threshold vertical location  910 . In some embodiments, the threshold vertical shift  908  indicates a greatest allowable shift of an autofocus window in the vertical direction. If a vertical location of an auto focus window is on or after the threshold vertical location  910 , it is considered that the motion detection window  904  is followed by the auto focus window after at least a threshold vertical distance. The threshold vertical distance may be used to determine whether to perform autofocusing, which is further described below with respect to  FIG. 11 . 
     The following description refers to the CPU  208  of a device  100  performing autofocus of the example image  902 . Though, in other embodiments, another processor or computing device may perform the autofocusing by implementing any combination of software, hardware, or firmware. The CPU  208  may perform autofocusing on one or more autofocus windows such as autofocus windows  0 ,  1 ,  2 , and  3  shown in  FIG. 9 . In particular, the CPU  208  determines whether a given autofocus window is suitable of being shifted for autofocusing by determining whether the motion detection window  904  is followed by a given autofocus window by at least the threshold vertical distance. As illustrated in  FIG. 9 , vertical locations of autofocus windows  0 ,  1 , and  2  are within the threshold vertical location  910 . Therefore, it may be determined that the autofocus windows  0 ,  1 , and  2  follow the motion detection window  904  within the threshold vertical distance. 
     The CPU  208  can perform autofocusing on autofocus window  3  if it is determined that the motion detection window  904  is followed by autofocus window  3  by at least the threshold vertical location  910 . The CPU  208  may perform autofocusing by shifting autofocus window  3  to a modified position, as indicted by autofocus window  3 ′. The CPU  208  determines a horizontal and vertical distance to shift the autofocus window  3  based on the motion vector of the motion detection window  904 . For example, the CPU  208  retrieves motion vector values from a register, where the motion vector values indicate amounts to shift based on cross-correlation scores in the horizontal and vertical directions. As illustrated in  FIG. 9 , the autofocus window  3 ′ may overlap a portion of the threshold vertical location  910 . In some embodiments, since the threshold vertical location  910  accounts for a threshold vertical shift  908 , the CPU  208  may shift an autofocus window upwards in the vertical direction by an amount no greater than the threshold vertical shift  908 . 
     Example Process Flows 
       FIG. 10  is a flowchart illustrating a method of generating a motion vector, according to one embodiment. Some embodiments may include different and/or additional steps, or perform the steps in different orders. 
     In one embodiment, the statistics circuit  402  determines  1002  row sums of pixel values of each block of pixels in (e.g., an motion detection window of) a current image. The statistics circuit  402  determines  1004  column sums of the pixel values of each block of pixels in the current image. In some embodiments, the statistics circuit  402  may perform the steps  1002 - 1004  in parallel for multiple color components or blocks of an input image. In some embodiments, the statistics circuit  402  determines row sums and column sums in an aggregation mode, for example, where pixel values are accumulated over adjacent pixels or blocks. 
     The VCA circuit  408  determines  1006  first cross-correlation scores between the row sums of the pixel values of each block of pixels in the current image with row sums of pixel values of each block of pixels in a prior image preceding the current image. The VCA circuit  408  determines  1008  second cross-correlation scores between the column sums of the pixel values of each block of pixels in the current image with column sums of the pixel values of each block of pixels in the prior image. Vectors of the current image may be greater in size than vectors of the prior image. For example, the row (or column) sums of the current image, or of the prior image, or both, may be cropped. 
     The VCA circuit  408  generates  1010  a motion vector for each block of pixels in the current image, e.g., by identifying a vertical shift corresponding to a greatest one of the first cross-correlation scores and a horizontal shift corresponding to a greatest one of the second cross-correlation scores. The motion vector may be stored in a register (e.g., for later retrieval for performing autofocusing) or output to another component such as a processor or memory of a corresponding device  100 . 
       FIG. 11  is a flowchart illustrating a method of performing autofocusing, according to one embodiment. Some embodiments may include different and/or additional steps, or perform the steps in different orders. 
     In one embodiment, a device  100  determines  1102  a current motion vector of a motion detection window in a current image. The current motion vector of the motion detection window may be determined by the motion estimator  305  using the process shown in  FIG. 10 . The motion detection window may have dimensions that are multiples of two, and may have a dimension of at least eight pixels in width and height. In some embodiments, current motion vectors may be determined for multiple motion detection windows (e.g., up to eight by eight) in a given image. 
     The CPU  208  of the device  100  determines  1104  a horizontal location and a vertical location of an autofocus window in the current image. For instance, as shown in of  FIG. 9 , the 2D image  902  includes multiple autofocus windows each having a different horizontal and vertical location, e.g., coordinates in the X and Y axis. The CPU  208  determines  1106  whether the motion detection window is followed by the autofocus window after at least a threshold vertical distance, where the threshold vertical distance accounts for at least a period of time for determining the current motion vector. In some embodiments, the CPU  208  selects the autofocus window from a set of multiple autofocus windows. Referring to the example shown in  FIG. 9 , the CPU  208  may select autofocus window  3  from the other autofocus windows  0 ,  1 , and  2 , if it is determined that autofocus window  3  is outside of the threshold vertical location  910 . In other embodiments, the CPU  208  may process up to sixteen or more autofocus windows for a given image. 
     If it is determined that the motion detection window is followed by the autofocus window after at least the threshold vertical distance, the CPU  208  performs autofocusing  1108  by adjusting at least one property of the autofocus window using the current motion vector. The property of the autofocus window may include a location, shape, size, or orientation of the autofocus window. For example, the CPU  208  may shift the horizontal of location of the autofocus window by a horizontal element of the current motion vector. Additionally, the CPU  208  may shift the vertical location of the autofocus window by a vertical element of the current motion vector. The CPU  208  may shift the location of the autofocus window along a vertical axis by an amount less than or equal to a threshold vertical shift, and along a horizontal axis by an amount less than or equal to a threshold horizontal shift. The threshold vertical shift and threshold horizontal shift may each be a multiple of two. In some embodiments, the shifted location of the autofocus window overlaps within the threshold vertical location  910  (as shown in the example of  FIG. 9 ). In other embodiments, adjusting the at least one property includes one or more of: rotating the autofocus window by a certain degree, modifying a shape of the autofocus window (e.g., from a square to a different type of quadrilateral or polygon), or increasing or decreasing a size of the autofocus window. 
     In some embodiments, if it is determined that the motion detection window is followed by the autofocus window within the threshold vertical distance, the CPU  208  performs autofocusing  1110  by adjusting at least one property of a different autofocus window using a motion vector corresponding to the different autofocus window. For example, in the embodiment shown in  FIG. 9 , the CPU  208  may perform AF statistics based on un-shifted autofocus windows  0 ,  1 , or  2 , and based on shifted autofocus window  3 . The at least one property may include any of the example properties described above. 
     In other embodiments, if it is determined that the motion detection window is followed by the autofocus window within the threshold vertical distance, the CPU  208  generates a signal indicating that the current motion vector is not to be used for performing autofocusing on the autofocus window, e.g., to avoid back pressure on the VCA circuit  408  or for read/write operations via DMA. The signal may be an interrupt, which occurs after processing of a given autofocus window, rather than at the end processing of an image frame. The CPU  208  may generate an interrupt or signal for each active autofocus window of the image. 
     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.

Metadata:
Filing Date: 20180810
Publication Date: 20200128
Grant Date: 20200128
Priority Date: 20180810
Inventors: WANG, MUGE
LIN, SHENG
KU, WEICHUN
CHANG, HSIAO-CHEN
TAO, YIXIANG
Assignee: APPLE INC
CPC Classifications: [{"code": "G06T2207/20021", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10024", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06T2207/10016", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04N19/176", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N1/6019", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N19/513", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04N1/6019", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N19/176", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/0006", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04N19/513", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/002", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T7/246", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06T5/70", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06T3/02", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 69180136