IMAGE VISUAL QUALITY ASSESSMENT

A method of image quality assessment, performed by one or more processors in an image capture device, is disclosed. The method comprising receiving notification of capture of an image and in response, initiating an image assessment task to assess the quality of the image. The assessment task comprises determining suitability of the image for image quality assessment, running an image quality assessment model on the image to generate image quality assessment results, collecting data related to the capture of the image, and transmitting the results to an image quality assessment repository. The image assessment task may be a lower priority asynchronous task.

TECHNICAL FIELD

The present disclosure relates generally to the assessment of the quality of images captured by user image capture devices such as smartphones or tablets.

BACKGROUND

Current widespread use of image-sharing platforms such as messaging applications, including social media applications and media-sharing or posting applications, and the associated indicators of approval such as a thumbs up or a “like” by viewers of the image, has made the quality of images that are captured by user devices of particular importance. Good quality images promote enjoyment of and engagement with such platforms by people who have captured such images, as well as with recipients or other viewers of the images on the platform.

DETAILED DESCRIPTION

Automatic quality assessment for images is important to a wide variety of applications (e.g., capture pipelines, compression algorithms). However, the subjective nature of visual quality assessment makes it a challenging task, since traditional signal fidelity measures do not align well with human visual perception, mainly because not every distortion is equally noticeable. Further, the requirement of some image quality assessments of a full reference image (full-reference or “FR” models) is not possible to achieve in many cases. On the other hand, human subjective evaluation, is costly and hard to scale.

To address these challenges, proposed is a no-reference “NR” framework for objectively measuring image visual quality at scale without requiring a reference image, by generating a vision model that estimates or approximates human evaluation of image quality. The framework can be extended to cover different aesthetics (e.g., different country, culture and user group) and more-specific quality measurements (e.g., exposure, blurriness, color tone).

Instead of manually defining and extracting visual features, the framework directly feeds pixels into a deep neural network such as a convolutional neural network. This approach uses a large pre-trained image classification model (based for example on millions of images) and fine tunes the last layer of the neural network with human-scored image datasets (for example done on thousands of images). This framework provides good generalization ability and correlation with human evaluation. The framework turns the quality assessment problem into a machine learning problem that requires little domain knowledge, as visual features are automatically extracted from the neural network.

In some examples, the model takes image pixels in RGB space as input and predicts multiple image quality score distributions. The proposed quality scores are for overall quality, exposure, blurriness, color tone and noise. These quality scores are key factors for smartphone camera photos. Predicting multiple quality scores in the same network can significantly reduce model size and inference cost while not sacrificing model performance. The model is trained to estimate the distribution of the empirical human evaluation scores. For each score, the output is a probability mass function p=[p1, p2, . . . , pn] where n denotes the number of score buckets. Predicting score distribution is more effective than regressing images to mean evaluation scores. In using the model to evaluate the image quality, the mean of the predicted scores can be used as a single indicator.

In use of the framework, to estimate the image quality of a new image, model inference is run on the client or user device on which the image is captured. To avoid introducing latency increases to key performance metrics in the image capture pipeline, the quality assessment runs in an asynchronous manner that does not block or otherwise interfere with the image capture pipeline. The assessment task is initiated in a background or lower priority thread after the image has been captured. The image quality score is then reported independently, along with other camera or device events and parameters for use in the data analysis phase.

In some examples, provided is a method of image quality assessment, performed by one or more processors in an image capture device, including receiving notification of capture of an image, initiating an image quality assessment task to assess the quality of the image, the image quality assessment task includes determining suitability of the image for image quality assessment, running an image quality assessment model on the image to generate image quality assessment results, collecting data related to the capture of the image, and transmitting the image quality assessment results and the related data to an image quality assessment repository.

The image quality assessment task may be a lower priority asynchronous task. The image quality assessment model may be a machine learning model trained using an image classifier network trained on a dataset of general images, the machine learning model including a layer trained on image-quality rated images as a feature output layer.

Determining the suitability of the image may include identifying that at least one face is represented in the image, or identifying a specific device camera used to capture the image. Determining the suitability of the image includes verifying that image effects have not been applied to the image.

The data related to the capture of the image may include a model identifier, operating system, and operating system version of the image capture device. The data related to the capture of the image may include whether the image was discarded, saved or forwarded by the user.

In some examples, provided is a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations for image quality assessment according to any of the methods and limitations set forth above, the operations including but not limited to receiving notification of capture of an image, initiating an image quality assessment task to assess the quality of the image, the image quality assessment task includes determining suitability of the image for image quality assessment, running an image quality assessment model on the image to generate image quality assessment results, collecting data related to the capture of the image, and transmitting the image quality assessment results and the related data to an image quality assessment repository.

In some examples, provided is a computing apparatus comprising a processor and a memory storing instructions that, when executed by the processor, configure the apparatus to perform operations for image quality assessment according to any of the methods and limitations set forth above, the operations including but not limited to receiving notification of capture of an image, initiating an image quality assessment task to assess the quality of the image, the image quality assessment task includes determining suitability of the image for image quality assessment, running an image quality assessment model on the image to generate image quality assessment results, collecting data related to the capture of the image, and transmitting the image quality assessment results and the related data to an image quality assessment repository.

Networked Computing Environment

FIG.1is a block diagram showing an example messaging system100for exchanging data (e.g., messages and associated content) over a network. The messaging system100includes multiple instances of a user device102, each of which hosts a number of applications, including a messaging client104and other applications106.

Each messaging client104is communicatively coupled to other instances of the messaging client104(e.g., hosted on respective other user devices102) and a messaging server system108via a network110(e.g., the Internet). A messaging client104can also communicate with locally-hosted applications106using Application Program Interfaces (APIs). The messaging system100also includes one or more user device102, which are communicatively coupled to the messaging server system108via the network110.

As used herein, the term messaging client104and messaging system100are deemed to include instant messaging platforms, social media platforms, content-sharing platforms, and other platforms in which a user forwards images to other users or posts images onto the platform.

A messaging client104is able to communicate and exchange data with other messaging clients104and with the messaging server system108via the network110. The data exchanged between messaging clients104, and between a messaging client104and the messaging server system108, includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data).

The messaging server system108provides server-side functionality via the network110to a particular messaging client104. While certain functions of the messaging system100are described herein as being performed by either a messaging client104or by the messaging server system108, the location of certain functionality either within the messaging client104or the messaging server system108may be a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system108but to later migrate this technology and functionality to the messaging client104where a user device102has sufficient processing capacity.

Turning now specifically to the messaging server system108, an Application Program Interface (API) server114is coupled to, and provides a programmatic interface to, application servers112. The application servers112are communicatively coupled to a database server118, which facilitates access to a database124that stores data associated with messages processed by the application servers112. Similarly, a web server126is coupled to the application servers112, and provides web-based interfaces to the application servers112. To this end, the web server126processes incoming network requests over the Hypertext Transfer Protocol (HTTP) and several other related protocols.

The Application Program Interface (API) server114receives and transmits message data (e.g., commands and message payloads) between the user device102and the application servers112. Specifically, the Application Program Interface (API) server114provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client104in order to invoke functionality of the application servers112. The Application Program Interface (API) server114exposes various functions supported by the application servers112, including account registration, login functionality, the sending of messages, via the application servers112, from a particular messaging client104to another messaging client104, the sending of media files (e.g., images or video) from a messaging client104to a messaging server116, and for possible access by another messaging client104, the settings of a collection of media data (e.g., story), the retrieval of a list of friends of a user of a user device102, the retrieval of such collections, the retrieval of messages and content, the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph), the location of friends within a social graph, and opening an application event (e.g., relating to the messaging client104).

The application servers112host a number of server applications and subsystems, including for example a messaging server116, an image processing server120, and a social network server122. The messaging server116implements a number of message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the messaging client104. As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available to the messaging client104. Other processor and memory intensive processing of data may also be performed server-side by the messaging server116, in view of the hardware requirements for such processing.

The application servers112also include an image processing server120that is dedicated to performing various image processing operations, typically with respect to images or video within the payload of a message sent from or received at the messaging server116.

The social network server122supports various social networking functions and services and makes these functions and services available to the messaging server116. To this end, the social network server122maintains and accesses an entity graph within the database124. Examples of functions and services supported by the social network server122include the identification of other users of the messaging system100with which a particular user has relationships or is “following,” and also the identification of other entities and interests of a particular user.

The messaging client104can notify a user of the user device102, or other users related to such a user (e.g., “friends”), of activity taking place in one or more external resources, for example games or slimmed down versions of third party applications. For example, the messaging client104can provide participants in a conversation (e.g., a chat session) in the messaging client104with notifications relating to the current or recent use of an external resource by one or more members of a group of users. One or more users can be invited to join in an active external resource or to launch a recently-used but currently inactive (in the group of friends) external resource. The external resource can provide participants in a conversation, each using respective messaging clients104, with the ability to share an item, status, state, or location in an external resource with one or more members of a group of users into a chat session. The shared item may be an interactive chat card with which members of the chat can interact, for example, to launch the corresponding external resource, view specific information within the external resource, or take the member of the chat to a specific location or state within the external resource. Within a given external resource, response messages can be sent to users on the messaging client104. The external resource can selectively include different media items in the responses, based on a current context of the external resource.

The messaging system100in the illustrated example includes an image quality assessment server128as part of messaging server system108, although image quality assessment server128may alternatively be provided as part of a separate server system. The image quality assessment server128receives image quality assessment results and the related data transmitted from the user devices102, and performs analysis and data mining on the cumulative image quality assessment results to generate image-quality insights.

System Architecture

FIG.2is a block diagram illustrating further details regarding the messaging system100, according to some examples. Specifically, the messaging system100is shown to comprise the messaging client104and the application servers112. The messaging system100embodies a number of subsystems, which are supported on the client-side by the messaging client104and on the sever-side by the application servers112. These subsystems include, for example, an ephemeral timer system202, a collection management system204, an augmentation system208, a map system210, a game system212, and an image quality reporting system214.

The ephemeral timer system202is responsible for enforcing the temporary or time-limited access to content by the messaging client104and the messaging server116. The ephemeral timer system202incorporates a number of timers that, based on duration and display parameters associated with a message, or collection of messages (e.g., a story), selectively enable access (e.g., for presentation and display) to messages and associated content via the messaging client104. Further details regarding the operation of the ephemeral timer system202are provided below.

The collection management system204furthermore includes a curation interface206that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface206enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, the collection management system204employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain examples, compensation may be paid to a user for the inclusion of user-generated content into a collection. In such cases, the collection management system204operates to automatically make payments to such users for the use of their content.

In some examples, the augmentation system208provides a user-based publication platform that enables users to select a geolocation on a map and upload content associated with the selected geolocation. The user may also specify circumstances under which a particular media overlay should be offered to other users. The augmentation system208generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.

The map system210provides various geographic location functions, and supports the presentation of map-based media content and messages by the messaging client104. For example, the map system210enables the display of user icons or avatars on a map to indicate a current or past location of “friends” of a user, as well as media content (e.g., collections of messages including photographs and videos) generated by such friends, within the context of a map. For example, a message posted by a user to the messaging system100from a specific geographic location may be displayed within the context of a map at that particular location to “friends” of a specific user on a map interface of the messaging client104. A user can furthermore share his or her location and status information (e.g., using an appropriate status avatar) with other users of the messaging system100via the messaging client104, with this location and status information being similarly displayed within the context of a map interface of the messaging client104to selected users.

The game system212provides various gaming functions within the context of the messaging client104. The messaging client104provides a game interface providing a list of available games that can be launched by a user within the context of the messaging client104, and played with other users of the messaging system100. The messaging system100further enables a particular user to invite other users to participate in the play of a specific game, by issuing invitations to such other users from the messaging client104. The messaging client104also supports both the voice and text messaging (e.g., chats) within the context of gameplay, provides a leaderboard for the games, and also supports the provision of in-game rewards (e.g., coins and items).

The image quality reporting system214provides the image quality determination after image capture by the user device102, as described in more detail below.

FIG.3is a schematic diagram illustrating an image quality assessment model300according to some embodiments. As illustrated in the figure, the quality assessment model300contains three major components, a baseline image classifier network302, multiple FC layers304(“fully-connected” layers) and multiple softmax layers306The image classifier network302is a known pre-trained network with weights initialized by training on a dataset of millions of images such as ImageNet. Any commonly used image classification models such as Inception, MobileNet can be used, with the particular model being chosen depending on the required model accuracy and inference performance (latency, power consumption, and so forth).

In assessment model300, the last layer of the image classifier network302has been selected to use as a feature output layer to the FC layers304. The last layer of the image classifier network302is good for use as the feature output layer as it provides the most abstract features. However, an earlier layer of the image classifier network302may be selected as a feature output layer, for quality tasks like noise and blurriness, as smaller patches might have better features for use in these determinations.

The FC layers304take the high-level abstract features generated from the image classifier network302and output logits for each head. The FC layers304include trained parameters that translate image features into unnormalized quality scores. The FC layers304are trained as described below with reference toFIG.4. The softmax layers306normalize the logits and provide a predicted probability distribution.

In machine learning, the loss function is determined as the difference between the actual output and the predicted output from the model for a single training example, while the average of the loss function for all the training examples is known as the cost function. For a given single training image, the output is a probability distribution of human scores with N ordered buckets. Because of the added softmax layers306, the output of the assessment model300is also a probability distribution, satisfying SUM(pi)=1, 1<=i<=N. The most common loss function used to measure the difference between two probability distributions is cross-entropy loss. However, cross-entropy loss lacks the inter-class relationships between score buckets, and the strict order information of the score buckets is lost in the cross-entropy loss function. Therefore, the assessment model300uses the squared Earth Mover's Distance (EMD) loss, which is defined as the minimum cost to move the mass of one distribution to another.

The FC layers304are trained as below using a training dataset of a few thousand images that have been scored by human scorers. To reduce overfitting issues resulting from the relatively small data set, image data augmentation techniques that do not significantly change the image composition are used to increase the set size, for example cropping and horizontal flipping of scored images.

FIG.4is a schematic diagram illustrating a training system architecture400according to some embodiments. The architecture400generally comprises four loosely-coupled components, a data pipeline402, a training engine404, model delivery system406and client inference process408. The architecture generates a dataset with pre-defined evaluation guidance, a training a model from this dataset, delivers the model to a user device102and runs client inference on the user device102to generate a quality score from a captured image410on the user device102.

The image labeling system412provides a platform for adding human-specified quality evaluation labels to images from an image set414. The resulting training dataset418comprises labeled images with verified quality assessments that can be used to train and assess the assessment model300. Human annotators use the image labeling system412to evaluate image quality-based factors such as blurriness and noise. The image labeling system412also supports image evaluations intersections, in which multiple annotators annotate the same image.

Annotators make subjective judgments following evaluation guidance416. The evaluation guidance416provides a definition of what each score represents and ensures consistency, as far as possible, between different annotators. Also included is a dataset validator420, which checks the training dataset418to ensure that the training dataset418has data quality, namely that the images and labels in the training dataset41are both correct and meaningful. The dataset validator420, uses validation constraints to check for correctness, meaningfulness, and security of data in the training dataset418.

The training engine404comprises a perceptron trainer422, a model repository426and a model evaluator424.

The perceptron trainer422is a config-driven end-to-end training API library together with a model zoo written in Tensorflow2, which can train a large image classification model and support transfer learning. The trained model generated by the perceptron trainer422is stored in the model repository426and can be converted to the compressed fast-dnn format that is supported by the client inference process408. In operation, the training process run by the perceptron trainer422is specified by a configuration file that defines how the training process should run, and operates on the labeled training dataset418. The perceptron trainer422also supports injecting raw Tensorflow codes for flexibility.

Models generated by the perceptron trainer422are evaluated by the model evaluator424. The model evaluator424operates on data that has not been seen by the perceptron trainer422, to verify whether and how well generated models actually work, and whether their assessment scores can be trusted. The model evaluator424can use any known model evaluation technique, such as holdout or cross-validation, using known classification metrics.

The model delivery system406provides a platform for delivering the correct version of the trained model to the correct user device102. The model delivery system406includes a user interface that permits software engineers to update the models if needed, and a delivery system to transmit the model files reliably to user devices102.

The trained model is run on captured images410in the client inference process408on a user device102. To avoid introducing latency increases to key performance metrics of the user device102, the quality assessment task performed by the client inference process408runs in an asynchronous, non-blocking manner after an image has been captured. The assessment task is initiated in a low-priority background thread after all the steps in the image capture pipeline have been completed. Once the quality score is calculated for an image, it is reported independently by the user device102to the data analysis system428along with related camera events and parameters, and device parameters. The reported quality score is not related to a specifically-identifiable user device102or user account.

FIG.5is a flowchart500illustrating a method of performing image quality assessment according to some examples. For explanatory purposes, the operations of the flowchart500are described herein as occurring in serial, or linearly. However, multiple operations of the flowchart500may occur in parallel. In addition, the operations of the flowchart500need not be performed in the order shown and/or one or more blocks of the flowchart500need not be performed and/or can be replaced by other operations. Furthermore, while the operations in flowchart500are described with reference to the client inference process408running on a user device102, the associated functionality may alternative be provided as part of a distributed processing system including one or more servers.

The flowchart commences at operation502with initiation of image capture by the user device102in response to the receipt of user input to capture an image. In some examples, the user input to capture the image is received by the messaging client104. In response to the capture of an image or in response to user input to capture an image, the messaging client104(or other application106) on the user device102launches an image quality assessment task in operation504. Since the speed and timing of the image quality assessment task is unrelated to the user experience, it runs asynchronously with the image capture pipeline in a low priority thread, to avoid affecting the user experience.

In operation506the image quality assessment task determines whether or not an image meets any required parameters. The particular parameters for determining suitability of the image depend on the application. For example, for the messaging system100, the image quality assessment may require that the image be a “selfie” image, since the quality of such images may be of particular relevance to the user of the user device102and thus to the operator of the messaging system100. That the image is a selfie can be verified by the image assessment task checking that the image was captured by a user-facing camera on the user device102and by determining, using image recognition techniques, that only one, or two or less, faces are present in the image. Other parameters may also be verified, for example, that the image does not include any augmented reality effects or other image modifications, which would skew the image quality results.

If the image does not meet the required parameters in operation506, the image assessment task ends at operation508. If the image does meet the required parameters in operation510, the image assessment task runs the trained image quality assessment model300on the image to generate multiple image quality score distributions as described above with reference toFIG.3. The score distributions can relate to various quality factors such as blurriness and image noise.

In operation512, the image assessment task collects relevant data related to the captured image. This can for example be the model name, operating system and OS version of the user device102, the version of the messaging client104, whether the image was discarded, saved or forwarded by the user, flash activation, zoom level, an image timestamp, and so forth.

In operation514the image quality results and the related data are transmitted by the messaging client104to the data analysis system428, where, together with image quality results and related data from other images captured with other user devices102, the data analysis system428can perform analysis and data mining on the cumulative results to generate image-quality insights. The flowchart500then ends at operation516until another image capture is initiated by the user.

Machine Architecture

FIG.6is a diagrammatic representation of the machine600within which instructions610(e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine600to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions610may cause the machine600to execute any one or more of the methods described herein. The instructions610transform the general, non-programmed machine600into a particular machine600programmed to carry out the described and illustrated functions in the manner described. The machine600may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine600may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine600may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smartwatch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions610, sequentially or otherwise, that specify actions to be taken by the machine600. Further, while only a single machine600is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions610to perform any one or more of the methodologies discussed herein. The machine600, for example, may comprise the user device102or any one of a number of server devices forming part of the messaging server system108. In some examples, the machine600may also comprise both client and server systems, with certain operations of a particular method or algorithm being performed on the server-side and with certain operations of the particular method or algorithm being performed on the client-side.

The machine600may include processors604, memory606, and input/output I/O components602, which may be configured to communicate with each other via a bus640. In an example, the processors604(e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor608and a processor612that execute the instructions610. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. AlthoughFIG.6shows multiple processors604, the machine600may include a single processor with a single-core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

The memory606includes a main memory614, a static memory616, and a storage unit618, both accessible to the processors604via the bus640. The main memory606, the static memory616, and storage unit618store the instructions610embodying any one or more of the methodologies or functions described herein. The instructions610may also reside, completely or partially, within the main memory614, within the static memory616, within machine-readable medium620within the storage unit618, within at least one of the processors604(e.g., within the Processor's cache memory), or any suitable combination thereof, during execution thereof by the machine600.

In further examples, the I/O components602may include biometric components630, motion components632, environmental components634, or position components636, among a wide array of other components. For example, the biometric components630include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The motion components632include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope).

With respect to cameras, the user device102may have a camera system comprising, for example, front cameras on a front surface of the user device102and rear cameras on a rear surface of the user device102. The front cameras may, for example, be used to capture still images and video of a user of the user device102(e.g., “selfies”), which may then be augmented with augmentation data (e.g., filters) described above. The rear cameras may, for example, be used to capture still images and videos in a more traditional camera mode, with these images similarly being augmented with augmentation data. In addition to front and rear cameras, the user device102may also include a 360° camera for capturing 360° photographs and videos.

Further, the camera system of a user device102may include dual rear cameras (e.g., a primary camera as well as a depth-sensing camera), or even triple, quad or penta rear camera configurations on the front and rear sides of the user device102. These multiple cameras systems may include a wide camera, an ultra-wide camera, a telephoto camera, a macro camera and a depth sensor, for example.

The position components636include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies. The I/O components602further include communication components638operable to couple the machine600to a network622or devices624via respective coupling or connections. For example, the communication components638may include a network interface Component or another suitable device to interface with the network622. In further examples, the communication components638may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices624may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

The various memories (e.g., main memory614, static memory616, and memory of the processors604) and storage unit618may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions610), when executed by processors604, cause various operations to implement the disclosed examples.

The instructions610may be transmitted or received over the network622, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components638) and using any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions610may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to-peer coupling) to the devices624.

Software Architecture

FIG.7is a block diagram700illustrating a software architecture704, which can be installed on any one or more of the devices described herein. The software architecture704is supported by hardware such as a machine702that includes processors720, memory726, and I/O components738. In this example, the software architecture704can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture704includes layers such as an operating system712, libraries710, frameworks708, and applications706. Operationally, the applications706invoke API calls750through the software stack and receive messages752in response to the API calls750.

The operating system712manages hardware resources and provides common services. The operating system712includes, for example, a kernel714, services716, and drivers722. The kernel714acts as an abstraction layer between the hardware and the other software layers. For example, the kernel714provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services716can provide other common services for the other software layers. The drivers722are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers722can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., USB drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

The libraries710provide a common low-level infrastructure used by the applications706. The libraries710can include system libraries718(e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries710can include API libraries724such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries710can also include a wide variety of other libraries728to provide many other APIs to the applications706.

The frameworks708provide a common high-level infrastructure that is used by the applications706. For example, the frameworks708provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks708can provide a broad spectrum of other APIs that can be used by the applications706, some of which may be specific to a particular operating system or platform.

In an example, the applications706may include a home application736, a contacts application730, a browser application732, a book reader application734, a location application742, a media application744, a messaging application746, a game application748, and a broad assortment of other applications such as a third-party application740. The applications706are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications706, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application740(e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application740can invoke the API calls750provided by the operating system712to facilitate functionality described herein.

Glossary