Patent Publication Number: US-11030726-B1

Title: Image cropping with lossless resolution for generating enhanced image databases

Description:
BACKGROUND 
     Field 
     The present disclosure generally relates to enhancing the quality of a database by suitably modifying existing images in the database. More specifically, the present disclosure relates to identifying image crops from existing images in a database as candidates for lossless resolution enhancement, and storing the lossless enhanced image crops as new resources in the database. 
     Description of the Related Art 
     Current image databases have the limitation of the resolution of devices used to collect each of the images and the compression algorithms used for storage and processing. Typically, this limitation results in a heterogeneous quality of the stored images, and a limited thematic resource and versatility. 
     SUMMARY 
     In one embodiment of the present disclosure, a computer-implemented method is described, including selecting, in a server, a first image portion from an image. The computer-implemented method also includes identifying a known similar image associated with the first image portion, and determining a first score for enhancing the first image portion based on the known similar image. The computer-implemented method includes increasing a pixel resolution in the first image portion according to the scale to form an enhanced image portion. The computer-implemented method also includes identifying a synthetic value for the enhanced image portion and storing the enhanced image portion in a database when the synthetic value is below a tolerance value. 
     In one embodiment of the present disclosure, a computer-implemented method is described that includes selecting multiple images in a training set and selecting, for each of the images, an image portion. The computer-implemented method also includes determining a score for each image portion based on a known similar image and a test coefficient and modifying the image portion to obtain an enhanced image portion with an increased pixel resolution when the score is higher than a pre-selected value. The computer-implemented method also includes identifying a quality value of the enhanced image portion and modifying the test coefficient when the quality value is less than a tolerance value. 
     According to one embodiment, a system is described that includes one or more processors and a memory coupled to the one or more processors, the memory including instructions that, when executed by the one or more processors, cause the one or more processors to select, in a server, a first image portion from an image. The one or more processors also execute instructions to identify a known similar image to the first image portion, and to determine a first score for enhancing the first image portion based on the known similar image. The one or more processors execute instructions to increase a pixel resolution in the first image portion according to the scale to form an enhanced image portion. The one or more processors also execute instructions to identify a synthetic value for the enhanced image portion, and to store the enhanced image portion in a database when the synthetic value is below a tolerance value. 
     According to one embodiment, a non-transitory, machine-readable medium is described that includes instructions, which when executed by one or more processors, cause a computer to perform a method for executing the commands causing the system to perform a method that includes selecting, in a server, a first image portion from an image and identifying a known similar image associated with the first image portion. The method also includes determining a first score for enhancing the first image portion based on the known similar image. The method also includes increasing a pixel resolution in the first image portion according to the scale to form an enhanced image portion, identifying a synthetic value for the enhanced image portion, and storing the enhanced image portion in a database when the synthetic value is below a tolerance value. 
     In yet other embodiment, a system is described that includes a means for storing commands and a means for executing the commands causing the system to perform a method that includes selecting, in a server, a first image portion from an image and identifying a known similar image associated with the first image portion. The method also includes determining a first score for enhancing the first image portion based on the known similar image. The method includes increasing a pixel resolution in the first image portion according to the scale to form an enhanced image portion, identifying a synthetic value for the enhanced image portion, and storing the enhanced image portion in a database when the synthetic value is below a tolerance value. 
     It is understood that other configurations of the subject technology will become readily apparent to those skilled in the art from the following detailed description, wherein various configurations of the subject technology are shown and described by way of illustration. As will be realized, the subject technology is capable of other and different configurations and its several details are capable of modification in various other respects, all without departing from the scope of the subject technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are included to provide further understanding and are incorporated in and constitute a part of this specification, illustrate disclosed embodiments and together with the description serve to explain the principles of the disclosed embodiments. In the drawings: 
         FIG. 1  illustrates an example architecture suitable for updating an image crop, according to some embodiments. 
         FIG. 2  is a block diagram illustrating an example server and client from the architecture of  FIG. 1 , according to certain aspects of the disclosure. 
         FIG. 3  illustrates an embedded space with image vectors for enhancing resolution in cropped image portions, according to some embodiments. 
         FIG. 4  illustrates image portions cropped from a homogenous source image to obtain enhanced image portions, according to some embodiments. 
         FIG. 5  illustrates image portions cropped from a heterogeneous source image to obtain enhanced image portions, according to some embodiments. 
         FIG. 6  is a flow chart illustrating steps in a method for selecting an image portion from a source image to generate an enhanced image portion, according to some embodiments. 
         FIG. 7  is a flow chart illustrating steps in a method for training a tool for automatic identification of image crops to generate new images, according to some embodiments. 
         FIG. 8  is a block diagram illustrating an example computer system with which the client and server of  FIGS. 1 and 2 , and the methods of  FIGS. 6-7  can be implemented. 
     
    
    
     In the figures, elements and steps denoted by the same or similar reference numerals are associated with the same or similar elements and steps, unless indicated otherwise. 
     DETAILED DESCRIPTION 
     In the following detailed description, numerous specific details are set forth to provide a full understanding of the present disclosure. It will be apparent, however, to one ordinarily skilled in the art, that the embodiments of the present disclosure may be practiced without some of these specific details. In other instances, well-known structures and techniques have not been shown in detail so as not to obscure the disclosure. 
     General Overview 
     As used herein, the term “content item” may be used, for example, in reference to a digital file that is composed of one or more media elements of different types (text, image, video, audio, etc.). A content item can be a single picture or a single video file. The term “image identifier” as used herein may refer to a form of metadata such as a tag and a label, or a search query associated with an image for identifying the image. 
     The present disclosure relates to the selective use of cropping functions with resolution enhancement tools to obtain high quality images from an existing database, and thus increase the resources of the database by storing the enhanced cropped images as new image resources. Currently, it is possible to identify certain features (e.g., people, cars) in images. However, image recognition algorithms typically lack the ability to select crops from an image and enhance the selection to create new, marketable images (e.g., images that can be stored in a database and downloaded by multiple users). Embodiments as disclosed herein resolve the above technical problem by providing a learning model that has the ability to compare among different candidate image portions (or “crops”). The learning model uses performance data for the image enhancement of different crops to select the crops that render better, or the best results. In some embodiments, the learning model makes a fully automated selection of candidate crops, over multiple cropping scale ranges. 
     Given an image, the learning model identifies appropriate crops regions using a similar image type selected from the database. The image portion resulting from image cropping has a reduced resolution, typically below quality standards. To resolve this problem, some embodiments re-scale the image crops to obtain a desired resolution using a Generative Adversarial Network (GAN) tool. This process allows the automatic identification of a better image crop produced at a desired higher resolution. Some implementations provide a front-end portal that users can access remotely using a mobile device or a workstation. Through the front-end portal, a user can select a specific image crop by hand, and test whether an enhanced image portion produces an image that the user desires to download, or apply in a graphic design. 
     Embodiments as disclosed herein provide a solution to the problem arising in the realm of computer technology of generating and updating image databases for search engines and graphic editors. The solution relies on fully automated identification of optimal image crops for the generation of new images. To optimize the identification process, some embodiments use image enhancement engines based on similar image comparisons and appropriately weighting the performance of multiple results for selecting the better results. In some embodiments, the criteria for selecting suitable image crops are flexible. In some embodiments, a selection criterion includes a predicted performance of new image crops based on the interest that the enhanced image crop may generate. The interest may be measured as downloads, clicks, or sales of the enhanced image crop and closely related images. Some embodiments include a feedback loop for the learning model to allow further improvements from previous predictions. Selected candidate image crops are processed (e.g., using a GAN tool) to improve resolution to desired resolution. Performance of these new images is monitored to modify and adjust the learning model with new predictions for image quality. 
     The proposed solution further provides improvements to the functioning of the computer itself, because it creates an expanded resource of images for an image search engine. The availability of an almost endless image resource also reduces the computational cost of a search engine, and provides a tool for equalization of image quality across the database. 
     Example System Architecture 
       FIG. 1  illustrates an example architecture  100  for an image search engine suitable for practicing some implementations of the disclosure. Architecture  100  includes servers  130  and client devices  110  connected over a network  150 . One of the many servers  130  is configured to host a memory including instructions which, when executed by a processor, cause the server  130  to perform at least some of the steps in methods as disclosed herein. In some embodiments, the processor is configured to identify one or more image portions that may be automatically converted in separate, enhanced images for use in a database. The processor may use a suitably trained image enhancement engine, including a GAN tool. The GAN tool may include a generative tool to merge component images into a synthetic image (e.g., an image with a higher resolution). The component images may include a low-resolution image crop and a known similar image having a higher resolution. The known similar image may be retrieved from the database. The GAN tool may also include a discriminative tool to evaluate a synthetic detectability of the synthetic image (e.g., the resemblance of the synthetic image with a real image from the image database). For purposes of load balancing, multiple servers  130  can host memories including instructions to one or more processors, and multiple servers  130  can host a history log and the second image database. 
     Servers  130  may include any device having an appropriate processor, memory, and communications capability for hosting the generative tool, the discriminative tool, a GAN tool, and an image enhancement engine hosting the tools. The image enhancement engine may be accessible by various clients  110  over the network  150 . Clients  110  can be, for example, desktop computers, mobile computers, tablet computers (e.g., including e-book readers), mobile devices (e.g., a smartphone or PDA), or any other devices having appropriate processor, memory, and communications capabilities for accessing the image search engine and the database on one or more of servers  130 . Network  150  can include, for example, any one or more of a local area tool (LAN), a wide area tool (WAN), the Internet, and the like. Further, network  150  can include, but is not limited to, any one or more of the following tool topologies, including a bus network, a star network, a ring network, a mesh network, a star-bus network, tree or hierarchical network, and the like. 
       FIG. 2  is a block diagram  200  illustrating an example server  130  and client  110  in the architecture  100  of  FIG. 1 , according to certain aspects of the disclosure. Client  110  and server  130  are communicatively coupled over network  150  via respective communications modules  218 - 1  and  218 - 2  (hereinafter, collectively referred to as “communications modules  218 ”). Communications modules  218  are configured to interface with network  150  to send and receive information, such as data, requests, responses, and commands to other devices on the network. Communications modules  218  can be, for example, modems or Ethernet cards. A user may interact with client device  110  via an input device  214  and an output device  216 . Input device  214  may include a mouse, a keyboard, a pointer, a touchscreen, a microphone, and the like. Output device may be a screen display, a touchscreen, a speaker, and the like. 
     Server  130  includes a memory  220 - 2 , a processor  212 - 2 , and communications module  218 - 2 . Processor  212 - 2  is configured to execute instructions, such as instructions physically coded into processor  212 - 2 , instructions received from software in memory  220 - 2 , or a combination of both. In some embodiments, memory  220 - 2  includes an image enhancement engine  240 . Image enhancement engine  240  may include GAN tool  242 . GAN tool  242  may include a deep vision tool  244 , a generative tool  246 , and a discriminative tool  248 . Deep vision tool  244  may include a context embedder for associating images into an embedded vector space  250  based on context (e.g., a figure caption, textual reference, or semantic significance). 
     Embedded space  250  is a multi-dimensional vector space wherein each dimension corresponds to a style class or feature for an image. Accordingly, a vector projection over each dimension indicates, for a given image, a degree to which the image includes or represents said style class. A style class may be defined by an image caption, or some other textual description of a semantic concept that is useful to distinguish one image from another, or classify two images as visually similar. The quantification of the number and type of style classifications, and the projection of image vectors on each of the style classifications for any given image, may be performed by training a non-linear algorithm such as deep vision tool  244 . The training of deep vision tool  244  is enhanced every time a user activates GAN tool  242 . In that regard, deep vision tool  244  maps variable sized images in the image database into image vectors in the embedded space having a predetermined vector dimension. In some embodiments, deep vision tool  244  is trained to form an image vector in embedded space  250 . 
     Generative tool  246  is configured to generate a plausible (e.g., ‘realistic’), synthetic image given a compositional constraint (e.g., from a vector in embedded space  250 ) and a random seed. Discriminative tool  248  is configured to determine whether the synthetic image is real or not, and whether or not it matches the compositional constraint. In some embodiments, GAN tool  242  includes a recurrent tool that receives as input, from the user of client device  110 , a sequence of image anchors as compositional constraints. In some embodiments, an image anchor is a box within a canvas having a location, a shape and a size, and a pre-selected content. The pre-selected content may include a specific image, a text query, or a full description of a desired theme within the box. More specifically, in some embodiments, at least one of GAN tool  242 , deep vision tool  244 , generative tool  246 , or discriminative tool  248  include a neural network algorithm including multiple layers having nodes and coefficients associated to each node. The coefficients are determined based on a training set, wherein the gradients of the coefficients are weighted according to a desired outcome. Some embodiments include training discriminating tool  248  by applying the gradients to its coefficients based on losses (e.g., errors and failed assessments) resulting from real images with matching descriptions, from real images with invalid descriptions, and synthetic images (e.g., provided by generative tool  246 ). Accordingly, in some embodiments, discriminative tool  248  is configured to access image database  252  for training, and generative tool  246  is configured to provide synthetic images to image database  252 . 
     The user may access GAN tool  242  through an application  222  or a web browser installed in memory  220 - 1 . Accordingly, application  222  may be installed by server  130  and perform scripts and other routines provided by server  130 . Execution of application  222  may be controlled by a processor  212 - 1  in client device  110 . In some embodiments, GAN tool  242 , the tools contained therein, and at least part of image database  252  may be hosted in a different server that is accessible by server  130 . 
       FIG. 3  illustrates an embedded space  350  with image vectors  335 - 1 ,  335 - 2 ,  335 - 3 ,  335 - 4 , and  335 - 5  (hereinafter, collectively referred to as “image vectors  335 ”), according to some embodiments. In some embodiments, each image vector  335  may be associated with an image in a database (e.g., image database  252 ). Conversely, in some embodiments, each image stored in the database may have an associated image vector  335  in embedded space  350 . A measure of distance between any two vectors A and B (e.g., vectors  335 ) may be defined as a “cosine” distance, D  353 . In some embodiments, and without limitation, a cosine distance between two vectors, A and B, regardless of the dimensions of the vectors, may be obtained from the mathematical expression 
     
       
         
           
             
               
                 
                   
                     D 
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                       1 
                       - 
                       
                         
                           A 
                           · 
                           B 
                         
                         
                           | 
                           A 
                           | 
                           | 
                           B 
                           | 
                         
                       
                     
                   
                   , 
                 
               
               
                 
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     where the “dot” product “□” may be suitably defined for the vector space that includes vectors A and B, and |A| and |B|, are generally “lengths” associated to vectors A and B, respectively, in the vector space. The lengths of vectors A and B may be defined in terms of coordinates X 1 (x) and X 2 (y) in different ways. For example, in some embodiments, the length of vectors A and B may be defined as:
 
| A |=√{square root over ( x   A   2   +y   A   2 )}  (2.1)
 
| B |=√{square root over ( x   B   2   +y   B   2 )}  (2.2)
 
     And the dot product A⊕B may be defined as
 
 A·B=x   A   ·x   B   +y   A   ·Y   B   (2.3)
 
     Image vectors  335  are associated, respectively, to source image  345 , image portion  355   a , known similar image  365   a , image portion  355   b , and known similar image  365   b . Without limitation, and for illustrative purposes only, source image  345  may include a city landscape. Image portion  355   a  may include an image of a car cropped from source image  345 . Known similar image  365   a  may be a generic automobile in the database. In some embodiments, known similar image  365   a  is a high-resolution image of the same or similar car depicted in image portion  355   a . Likewise, image portion  355   b  may include an image of a skyscraper cropped from a different portion in source image  345 . Accordingly, known similar image  365   b  may be a generic skyscraper in the database. 
     Image portions  355   a  and  355   b  (hereinafter, collectively referred to as “image portions  355 ”) may be automatically selected by image enhancement engine  240 . Accordingly, in an effort to increase and enhance the quality of image database  252 , image enhancement engine  240  may select different cropped portions of source image  345 , and build enhanced versions of the cropped portions. In some embodiments, image enhancement engine  240  may store the enhanced versions of the cropped portions in image database  252  as separate images, as a resource for further searches. In some embodiments, image portions  355  may be selected by a user through application  222  in client device  110 . The user may be interested in enhancing certain detail within source image  345  to extract more information, or even to create an improved version of source image  345 . 
     For illustrative purposes, only the projection of embedded space  350  on two dimensions, X 1 , in the abscissae and X 2 , in the ordinates, are illustrated. In some embodiments, embedded space  350  may include tens, hundreds, or even thousands of dimensions. In some embodiments, known similar image  365   a  may be selected from an image database. Accordingly, in some embodiments, generative tool  246  is used in combination with discriminative tool  248  (e.g., as configured in GAN tool  242 ) to enhance image portion  355   a , based on features in known similar image  365   a.    
     Embedded space  350  may be formed by deep vision tool  244  using image database  252 . Further, embedded space  350  may be stored in a server memory (e.g., embedded space  250  in memory  220 - 2 ), or may be external to the memory and directly or remotely accessible to GAN tool  242  (e.g., image database  252 ). The values for the coordinates along the X 1  and X 2  axes, for each of image vectors  335 , may be determined by deep vision tool  244 . The projections of image vectors  335  onto axes X 1  and X 2  indicate a degree to which images  345  include or represent a given feature in a classification space. For example, and without limitation, axis X 2  (ordinates) may indicate a degree of “car-like” features in an image. Accordingly, projection  370 - 1  of image vectors  335 - 1 ,  335 - 2 , and  335 - 3  onto axis X 2  are expected to be similar (e.g., a “car-like” value on coordinate X 2 ). More specifically, it is expected that a projection  370 - 2  of the cityscape in source image  345  on “car-like” axis X 2  be the same, or almost the same, as the projection of image portion  355   a  and of known similar image  365   a  onto axis X 2 . Likewise, axis X 1  (abscissae) may indicate a degree of “skyscraper-like” features in an image. Accordingly, projection  370 - 2  of image vectors  335 - 1 ,  335 - 4 , and  335 - 5  onto axis X 1  are expected to be similar. More specifically, it is expected that a projection  370 - 2  of the cityscape in source image  345  on “skyscraper-like” axis X 1  be the same, or almost the same, as the projection of image portion  355   b  and of known similar image  365   b  onto axis X 1 . 
     The system may encounter multiple similar images  365   a  to match image portion  355   a  using the cosine distance (cf. Eq. 1 using coordinates X 1  and X 2 ) to look for the closest images in embedded space  350 . Based on the value or quality of each of similar images  365   a , the system may predict the performance of image portion  355   a . Likewise, the performance of image portion  355   b  may be determined by the value or quality of each of similar images  365   b  selected by their proximity to image portion  355   b . In some embodiments, the system may compare the performance of image portion  355   a  to that of image portion  355   b  to determine which of the cropped images to use for enhancing the image database. Further, in some embodiments, the system may adjust the cropping parameters (e.g., increase/decrease, horizontal/vertical boundaries) to either (or both) image portions  355  to increase, maximize, or optimize, the performance (based on the performance of the similar images selected thereafter). Moreover, in some embodiments the system may combine image portions  355  into a single image (e.g., extend the crop boundaries to include both the skyscraper and the car), based on an increased performance of the resulting crop. 
       FIG. 4  illustrates image portions  455   a  and  455   b  (collectively referred to, hereinafter, as “image portions  455 ”) cropped from a homogeneous source image  445  to obtain enhanced image portions, according to some embodiments. Image portions  455  may be selected out of different crops in source image  445 . Source image  445  is homogenous in the sense that it contains a single item that can be simply described with a caption such as “Lion&#39;s Head.” Image portion  455   a  may be the “Lion&#39;s eye,” and image portion  455   b  may be the “Lion&#39;s mouth.” In some embodiments, image portions  455  may be automatically selected by an application running in the client device, or by code stored in a server memory (e.g., application  222  or image enhancement engine  240  and tools thereof). For example, in some embodiments, an application or image enhancement engine may be configured to expand, or augment, the image stock in a database (e.g., image database  252 ), with an enhanced version (e.g., a “zoom in” or a “close up”) of the Lion&#39;s eye or mouth. In some embodiments, the selection of image portions  455  may be provided by a user (e.g., via client device  110 ), simply by clicking and selecting with a pointer (e.g., a mouse, a stylus, or finger, as in input device  214 ) on source image  445 . 
     In some embodiments, image enhancement engine  240  may use multiple image portions  455  to select the best-enhanced image portion(s) result. In some embodiments, image enhancement engine  240  may test multiple image portions  455  and select one that has a reduced computational cost for image enhancement. Accordingly, image enhancement engine  240  compares image portions  455  with known similar images  465   a  and  465   b  (hereinafter, collectively referred to as known similar images  465 ) to determine a score for predicting the enhanced performance on each of the image portions  455 . The performance of each of the image portions  455  may include a quality value intrinsic to the image portion, or a value of the resulting image in the “image marketplace” (e.g., the number of expected downloads of the resulting image based on the downloads and other value parameters of similar images  465 ). In some embodiments, image enhancement engine  240  may scale the pixel resolution of image portions  455  according to the pixel resolution of known similar images  465 . For example, image portion  455   a  may include n pixels  467 - 1  through  467 - n  (hereinafter, collectively referred to as “pixels  467 ”), including a pixel  467 - j  (j&lt;n, n being any integer). Known similar image  465   a  may include pixels  475 - 1  through  475 - m , where m is an integer greater, or much greater, than n. Accordingly, a pixel  467 - j  in image portion  455   a  may be split into two or more pixels  475 - k  in known similar image  465   a . Likewise, image portion  455   b  may include n pixels  485 - 1  through  485 - p  (hereinafter, collectively referred to as “pixels  485 ”), including a pixel  485 - j  (j&lt;n, n being any integer). Known similar image  465   b  may include pixels  495 - 1  through  495 - q  (hereinafter, collectively referred to as “pixels  495 ”), where q is an integer greater, or much greater, than p. Accordingly, a pixel  485 - j  in image portion  455   b  may be split into two or more pixels  485 - k  in known similar image  465   b . More generally, any image portion  455  can be scaled to multiple different image resolutions to obtain a desired enhancement performance at a limited computational cost. 
     In some embodiments, image enhancement engine splits pixels  467 - j  and  485 - j  into multiple pixels  475 - k  and  495 - k  respectively, to produce enhanced image portions where each of the pixels  475 - k  (or  485 - k ) has a specific value selected to enhance the quality of image portions  455 . To achieve this, image enhancement engine  240  may use GAN tool  242  and the pixel values in known similar images  465 . For example, generative tool  246  may use pixels  467  as input to generate pixels  475  as output, while discriminative tool  248  tests the synthetic appearance of the results (e.g., a synthetic value of the enhanced image portion is below a tolerance value). In some embodiments, instead of using pixels  475  as a desired output, GAN tool  242  may select other quality metrics in a performance feedback loop. In the performance feedback loop, the pixel values are adjusted until a performance error is below a tolerance value. In some embodiments, the performance error may include a proximity to a high quality image in the embedded space, and a market value or other quality indicator of the high quality image. In some embodiments, the performance error may be an intrinsic property of the image, such as sharpness, contrast, blur, chromaticity (e.g., color matching, and the like). 
     In some embodiments, image enhancement engine  240  may adjust the resolution scale (e.g., increase or decrease the number of pixels of the enhanced image portion) to achieve faster convergence of the feedback loop. In some embodiments, image enhancement engine  240  may also alter the number and type of input pixels for image portions  455 . For example, in some embodiments, image enhancement engine  240  may change the crop of source image  445  (adding more horizontal pixels, or more vertical pixels, or re-centering the image portion over the source image) to improve image enhancement performance. The image enhancement performance may be determined by a quality value, by a computational cost, or a combination of both. Image enhancement engine  240  may store or modify one or more test coefficients in GAN tool  242 , deep vision tool  244 , generative tool  246 , and discriminative tool  248 , based on past performance for multiple source images  445  in a training set. 
       FIG. 5  illustrates image portions  555   a  and  555   b  (hereinafter, collectively referred to as “image portions  555 ”) cropped from a heterogeneous source image  545  to obtain enhanced image portions, according to some embodiments. Image portions  555  may be selected out of different crops in source image  545 . Source image  545  is heterogeneous in the sense that it contains multiple disparate items that make a multi-faceted thematic composition (a “cityscape”). Image portion  555   a  may be a cloud in a blue sky background, and image portion  555   b  may be a taxi cab turning a corner. In some embodiments, image portions  555  may be automatically selected by an application running in the client device, or by code stored in a server memory (e.g., application  222  or image enhancement engine  240  and tools thereof). For example, in some embodiments, an application or image enhancement engine may be configured to expand, or augment, the image stock in a database (e.g., image database  252 ) with an enhanced version (e.g., a “zoom in” or a “close up”) of the city sky, or of the taxi cab turning the corner. In some embodiments, the selection of image portions  555  may be provided by a user (e.g., via client device  110 ) simply by clicking and selecting with a pointer (e.g., a mouse, a stylus, or finger, as in input device  214 ) on source image  545 . For example, in some embodiments, a user may desire to obtain information such as the identity of the cab driver, the cab plates, and the like. 
     In some embodiments, image enhancement engine  240  may use multiple image portions  555  to select the best-enhanced image portion result. In some embodiments, image enhancement engine  240  may test multiple image portions  555  and may leverage a reduced computational cost with a higher value of the resulting image in the image marketplace. Accordingly, image enhancement engine  240  compares image portions  555  with known similar images  565   a  and  565   b  (hereinafter, collectively referred to as known similar images  565 ) to determine a score for predicting the enhanced performance on each of the image portions  555 . In some embodiments, image enhancement engine  240  may scale the pixel resolution of image portions  555  according to the pixel resolution of known similar images  565 . For example, image portion  555   a  may include n pixels  567 - 1  through  565 - n  (hereinafter, collectively referred to as “pixels  567 ”), including a pixel  567 - j  (j&lt;n, n being any integer). Known similar image  565   a  may include pixels  575 - 1  through  575 - m , where m is an integer greater, or much greater, than n. Accordingly, a pixel  567 - j  in image portion  555   a  may be split into two or more pixels  575 - k  in known similar image  565   a . Likewise, image portion  555   b  may include n pixels  585 - 1  through  585 - r  (hereinafter, collectively referred to as “pixels  585 ”), including a pixel  585 - j  (j&lt;n, n being any integer). Known similar image  565   b  may include pixels  595 - 1  through  595 - s  (hereinafter, collectively referred to as “pixels  595 ”), where s is an integer greater, or much greater, than r. Accordingly, a pixel  585 - j  in image portion  555   b  may be split into two or more pixels  595 - k  in known similar image  565   b . More generally, any image portion  555  can be scaled to multiple different image resolutions, to obtain a desired image enhancement at a limited computational cost. 
     In some embodiments, image enhancement engine splits pixels  567 - j  and  585 - j  into multiple pixels  575 - k  and  595 - k  respectively, to produce enhanced image portions where each of the pixels  575 - k  (or  585 - k ) has a specific value selected to enhance the quality of image portions  555 . To achieve this, image enhancement engine  240  may use GAN tool  242  and the pixel values in known similar images  565 . For example, generative tool  246  may use pixels  567  as input to generate pixels  575  as output, while discriminative tool  248  tests the synthetic appearance of the results (e.g., a synthetic value of the enhanced image portion is below a tolerance value). In some embodiments, instead of using pixels  575  as a desired output, GAN tool  242  may select other quality metrics in a performance feedback loop. In the performance feedback loop, the pixel values are adjusted until a performance error (as defined above) is below a tolerance value. 
     In some embodiments, image enhancement engine  240  may adjust the resolution scale (e.g., increase or decrease the number of pixels of the enhanced image portion) to achieve faster convergence of the feedback loop. In some embodiments, image enhancement engine  240  may also alter the number and type of input pixels for image portions  555 . For example, in some embodiments, image enhancement engine  240  may change the crop of source image  545  (adding more horizontal pixels, or more vertical pixels, or re-centering the image portion over the source image) to improve image enhancement performance. The image enhancement performance may be determined by a quality value, by a computational cost, or a combination of both. Image enhancement engine  240  may store or modify one or more test coefficients in GAN tool  242 , deep vision tool  244 , generative tool  246 , and discriminative tool  248 , based on past performance for multiple source images  545  in a training set. 
       FIG. 6  is a flow chart illustrating steps in a method  600  for selecting an image portion from a source image to generate an enhanced image portion, according to some embodiments. Method  600  may be performed at least partially by any one of multiple network servers hosting a collection of images, videos, and multimedia files (e.g., images and video clips), while communicating with any one of a plurality of client devices through a network (e.g., any one of servers  130  and any one of client devices  110  in network  150 ). The client devices may be handled by a user, wherein the user may be registered to a private account with the server, or may be a visitor to the server website or logged in a server application (e.g., application  222 ). At least some of the steps in method  600  may be performed by a computer having a processor executing commands stored in a memory of the computer (e.g., processors  212  and memories  220 ). Further, steps as disclosed in method  600  may include retrieving, editing, and/or storing files in an image database that is part of, or is communicably coupled to, the computer, using, inter-alia, an image enhancement engine hosting a GAN tool (e.g., database  252 , image enhancement engine  240 , and GAN tool  242 ). The GAN tool may include a deep vision tool to embed images into an embedded space, a generative tool to form synthetic images from merged vectors, and a discriminative tool to determine how realistic a synthetic image is (e.g., deep vision tool  244 , generative tool  246 , discriminative tool  248 , and embedded spaces  250  and  350 ). Methods consistent with the present disclosure may include at least some, but not all, of the steps illustrated in method  600 , performed in a different sequence. Furthermore, methods consistent with the present disclosure may include at least two or more steps as in method  600  performed overlapping in time, or almost simultaneously. 
     Step  602  includes selecting, in a server, a first image portion from an image in a database. In some embodiments, step  602  includes receiving the image from a user via a client device communicatively coupled to the server. In some embodiments, step  602  includes retrieving the image from the database upon a user request. 
     Step  604  includes identifying a known similar image(s) associated with the first image portion. In some embodiments, step  604  may include identifying an ensemble (e.g., more than one) of similar images to allow for a better estimation of performance. In some embodiments, step  604  includes parsing the image according to a salient feature in a classification space, and verifying that the enhanced image portion contains the salient feature. In some embodiments, step  604  includes selecting a second image portion from the image, determining a second score for enhancing the second image portion based on a second similar image from the database, and forming a second enhanced image portion based on the second image portion when the second score is greater than the first score. In some embodiments, step  604  includes receiving the known similar image from a user via a client device communicatively coupled to the server. In some embodiments, step  604  includes searching a database for an image similar to the first image portion. 
     Step  606  includes determining a first score for enhancing the first image portion based on the known similar image. In some embodiments, step  606  may include determining the first score based on the performance of the known similar image in the image market place (e.g., number of downloads, or clicks, by other users). In some embodiments, step  606  includes adjusting a scale for the first image portion according to an aspect ratio of the known similar image when comparing the first image portion and the known similar image. In some embodiments, step  606  includes using a generative adversarial network to increase a pixel resolution and obtain an enhanced image portion that resembles the known similar image and has a synthetic value below the tolerance value. 
     Step  608  includes increasing a pixel resolution in the first image portion according to the scale to form an enhanced image portion. In some embodiments, step  608  includes increasing a number of pixel rows or a number of pixel columns according to the aspect ratio of the known similar image in the enhanced image portion, and selecting a new pixel value to enhance a salient feature of the image in a classification space. In some embodiments, step  608  includes splitting at least one pixel in the first image portion into two or more enhanced pixels, each of the enhanced pixels having a different pixel value based on a salient feature of the image in a classification space. 
     Step  610  includes identifying a synthetic value for the enhanced image portion. In some embodiments, step  610  includes replacing the first image portion with the enhanced image portion in the image to form a new image, and determining a similarity between the image and the new image. Further, in some embodiments, step  610  may include determining a difference in the probability that the original image will be downloaded or selected by a user with the probability that the new image be downloaded or selected by the user. In some embodiments, step  610  may include predicting a marketplace performance of the enhanced image portion. For example, in some embodiments step  610  may include using image selection, downloads, average order value ($) as marketplace performance parameters. 
     Step  612  includes storing the enhanced image portion in a database when the synthetic value is below a tolerance value. In some embodiments, step  612  includes modifying a crop factor in the first image portion when the first score is less than a pre-selected threshold. For example, in some embodiments step  612  may include increasing/decreasing a horizontal/vertical dimension of a window crop to obtain a new image portion. 
       FIG. 7  is a flow chart illustrating steps in a method  700  for training a tool for automatic identification of image crops to generate new images, according to some embodiments. Method  700  may be performed at least partially by any one of multiple network servers hosting a collection of images, videos, and multimedia files (e.g., images and video clips), while communicating with any one of a plurality of client devices through a network (e.g., any one of servers  130  and any one of client devices  110  in network  150 ). The client devices may be handled by a user, wherein the user may be registered to a private account with the server, or may be a visitor to the server website or logged in a server application (e.g., application  222 ). At least some of the steps in method  700  may be performed by a computer having a processor executing commands stored in a memory of the computer (e.g., processors  212  and memories  220 ). Further, steps as disclosed in method  700  may include retrieving, editing, and/or storing files in an image database that is part of, or is communicably coupled to, the computer, using, inter-alia, an image enhancement engine hosting a GAN tool (e.g., database  252 , image enhancement engine  240 , and GAN tool  242 ). The GAN tool may include a deep vision tool to embed images into an embedded space, a generative tool to form synthetic images from merged vectors, and a discriminative tool to determine how realistic a synthetic image is (e.g., deep vision tool  244 , generative tool  246 , discriminative tool  248 , and embedded spaces  250  and  350 ). Further, in some embodiments, method  700  includes selecting and storing commands and instructions in the server memory, the commands being part of the image enhancement engine, the GAN tool, or any of the components thereof. Methods consistent with the present disclosure may include at least some, but not all, of the steps illustrated in method  700 , performed in a different sequence. Furthermore, methods consistent with the present disclosure may include at least two or more steps as in method  700  performed overlapping in time, or almost simultaneously. 
     Step  702  includes selecting multiple images in a training set. 
     In some embodiments, step  702  includes forming at least one composite image in the training set using a generative adversarial tool and multiple source images. 
     Step  704  includes selecting, for each of the images, an image portion. 
     Step  706  includes determining a score for each image portion based on a known similar image and a test coefficient. The test coefficient may be a parameter in a non-linear function for a node in a neural network algorithm for the generative tool or the discriminative tool. In some embodiments, step  706  may further include modifying the test coefficient when the enhanced image portion is different from the source images. In some embodiments, step  706  includes modifying the test coefficient when a discriminative tool determines that a synthetic value of the enhanced image portion is larger than the tolerance value. In some embodiments, step  706  includes configuring a generative tool to transform the image portion into the known similar image style. 
     Step  708  includes modifying the image portion to obtain an enhanced image portion with an increased pixel resolution when the score is higher than a pre-selected value. In some embodiments, step  708  includes modifying a test coefficient in a discriminative tool to obtain an enhanced image portion with an increased pixel resolution. 
     Step  710  includes identifying a quality value of the enhanced image portion. In some embodiments, step  710  includes selecting a probability that a user will download the enhanced image portion as a quality value based on a predicted performance of the enhanced image in the “marketplace”. In some embodiments, step  710  includes selecting the quality value by replacing the image portion with the enhanced image portion for each of the images to obtain enhanced images, and comparing the enhanced images with each of the images. In some embodiments, the quality value of the enhanced image portion is a predicted marketplace value of the enhanced image portion (e.g., number of downloads, clicks, purchases and the like). In some embodiments, step  710  may include determining the quality value based on the marketplace value of the one or more similar images. Further, in some embodiments, step  710  may include selecting the quality value as a difference in the probability that the original image will be downloaded or selected by a user with the probability that the new image, including the enhanced image portion, be downloaded or selected by the user. 
     Step  712  includes modifying the test coefficient when the quality value is less than a tolerance value. 
     Hardware Overview 
       FIG. 8  is a block diagram illustrating an exemplary computer system  800  with which the client  110  and server  130  of  FIGS. 1 and 2 , and the methods of  FIGS. 6 and 7  can be implemented. In certain aspects, the computer system  800  may be implemented using hardware or a combination of software and hardware, either in a dedicated server, or integrated into another entity, or distributed across multiple entities. 
     Computer system  800  (e.g., client  110  and server  130 ) includes a bus  808  or other communication mechanism for communicating information, and a processor  802  (e.g., processors  212 ) coupled with bus  808  for processing information. By way of example, the computer system  800  may be implemented with one or more processors  802 . Processor  802  may be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information. 
     Computer system  800  can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory  804  (e.g., memories  220 ), such as a Random Access Memory (RAM), a flash memory, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device, coupled to bus  808  for storing information and instructions to be executed by processor  802 . The processor  802  and the memory  804  can be supplemented by, or incorporated in, special purpose logic circuitry. 
     The instructions may be stored in the memory  804  and implemented in one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, the computer system  800 , and according to any method well known to those of skill in the art, including, but not limited to, computer languages such as data-oriented languages (e.g., SQL, dBase), system languages (e.g., C, Objective-C, C++, Assembly), architectural languages (e.g., Java, .NET), and application languages (e.g., PHP, Ruby, Perl, Python). Instructions may also be implemented in computer languages such as array languages, aspect-oriented languages, assembly languages, authoring languages, command line interface languages, compiled languages, concurrent languages, curly-bracket languages, dataflow languages, data-structured languages, declarative languages, esoteric languages, extension languages, fourth-generation languages, functional languages, interactive mode languages, interpreted languages, iterative languages, list-based languages, little languages, logic-based languages, machine languages, macro languages, metaprogramming languages, multi-paradigm languages, numerical analysis, non-English-based languages, object-oriented class-based languages, object-oriented prototype-based languages, off-side rule languages, procedural languages, reflective languages, rule-based languages, scripting languages, stack-based languages, synchronous languages, syntax handling languages, visual languages, with languages, and xml-based languages. Memory  804  may also be used for storing temporary variable or other intermediate information during execution of instructions to be executed by processor  802 . 
     A computer program as discussed herein does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network. The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. 
     Computer system  800  further includes a data storage device  806  such as a magnetic disk or optical disk, coupled to bus  808  for storing information and instructions. Computer system  800  may be coupled via input/output module  810  to various devices. Input/output module  810  can be any input/output module. Exemplary input/output modules  810  include data ports such as USB ports. The input/output module  810  is configured to connect to a communications module  812 . Exemplary communications modules  812  (e.g., communications modules  218 ) include networking interface cards, such as Ethernet cards and modems. In certain aspects, input/output module  810  is configured to connect to a plurality of devices, such as an input device  814  (e.g., input device  214 ) and/or an output device  816  (e.g., output device  216 ). Exemplary input devices  814  include a keyboard and a pointing device, e.g., a mouse or a trackball, by which a user can provide input to the computer system  800 . Other kinds of input devices  814  can be used to provide for interaction with a user as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, the feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, tactile, or brain wave input. Exemplary output devices  816  include display devices, such as an LCD (liquid crystal display) monitor, for displaying information to the user. 
     According to one aspect of the present disclosure, the client  110  and server  130  can be implemented using a computer system  800  in response to processor  802  executing one or more sequences of one or more instructions contained in memory  804 . Such instructions may be read into memory  804  from another machine-readable medium, such as data storage device  806 . Execution of the sequences of instructions contained in main memory  804  causes processor  802  to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory  804 . In alternative aspects, hard-wired circuitry may be used in place of or in combination with software instructions to implement various aspects of the present disclosure. Thus, aspects of the present disclosure are not limited to any specific combination of hardware circuitry and software. 
     Various aspects of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. The communication tool (e.g., network  150 ) can include, for example, any one or more of a LAN, a WAN, the Internet, and the like. Further, the communication tool can include, but is not limited to, for example, any one or more of the following tool topologies, including a bus network, a star network, a ring network, a mesh network, a star-bus network, tree or hierarchical network, or the like. The communications modules can be, for example, modems or Ethernet cards. 
     Computer system  800  can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Computer system  800  can be, for example, and without limitation, a desktop computer, laptop computer, or tablet computer. Computer system  800  can also be embedded in another device, for example, and without limitation, a mobile telephone, a PDA, a mobile audio player, a Global Positioning System (GPS) receiver, a video game console, and/or a television set top box. 
     The term “machine-readable storage medium” or “computer-readable medium” as used herein refers to any medium or media that participates in providing instructions to processor  802  for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as data storage device  806 . Volatile media include dynamic memory, such as memory  804 . Transmission media include coaxial cables, copper wire, and fiber optics, including the wires forming bus  808 . Common forms of machine-readable media include, for example, floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. The machine-readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter affecting a machine-readable propagated signal, or a combination of one or more of them. 
     To illustrate interchangeability of hardware and software, items such as the various illustrative blocks, modules, components, methods, operations, instructions, and algorithms have been described generally in terms of their functionality. Whether such functionality is implemented as hardware, software, or a combination of hardware and software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. 
     As used herein, the phrase “at least one of” preceding a series of items, with the terms “and” or “or” to separate any of the items, modifies the list as a whole, rather than each member of the list (i.e., each item). The phrase “at least one of” does not require selection of at least one item; rather, the phrase allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items. By way of example, the phrases “at least one of A, B, and C” or “at least one of A, B, or C” each refer to only A, only B, or only C; any combination of A, B, and C; and/or at least one of each of A, B, and C. 
     To the extent that the term “include,” “have,” or the like is used in the description or the claims, such term is intended to be inclusive in a manner similar to the term “comprise” as “comprise” is interpreted when employed as a transitional word in a claim. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. 
     A reference to an element in the singular is not intended to mean “one and only one” unless specifically stated, but rather “one or more.” All structural and functional equivalents to the elements of the various configurations described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and intended to be encompassed by the subject technology. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the above description. No clause element is to be construed under the provisions of 35 U.S.C. § 112, sixth paragraph, unless the element is expressly recited using the phrase “means for” or, in the case of a method clause, the element is recited using the phrase “step for.” 
     While this specification contains many specifics, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of particular implementations of the subject matter. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
     The subject matter of this specification has been described in terms of particular aspects, but other aspects can be implemented and are within the scope of the following claims. For example, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. The actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the aspects described above should not be understood as requiring such separation in all aspects, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. Other variations are within the scope of the following claims.