Patent Publication Number: US-10776907-B2

Title: Dynamic image resolution adjustment for rendering changes to graphical content

Description:
TECHNICAL FIELD 
     This disclosure relates generally to computer-implemented methods and systems for computer graphics processing. Specifically, the present disclosure involves dynamic image resolution adjustment for rendering changes to image content. 
     BACKGROUND 
     Some image manipulation applications provide functions that allow a user to manipulate an image displayed in a user interface, such as moving, rotating, blurring, or changing the zoom level of the image. An animation of the manipulation can be presented in the user interface to illustrate the process of the manipulation. For instance, if an image manipulation application receives a user input dragging the image along a path to a new position using a mouse, the image manipulation application displays a movement of the image concurrently with the dragging input by rendering the image at various points along the path in the user interface. 
     However, in some cases, the image manipulation application renders the image at the different points more slowly than the dragging input moves along the path. For instance, if a dragging input moves from a start point to an end point, a rendition of the image at the end point may occur a few second after the dragging input reaches the end point, resulting in a noticeable lag between the dragging input and corresponding image rendition. This problem can be caused by, for example, insufficient computing resources on the computing device executing the image manipulation application, the large file size of the image, the high resolution of the image, the large number of layers in the images, the high complexity of the image operations, and so on. 
     Existing techniques address this issue by skipping events of the input device and discarding all input device events between two image renditions. As a result, images are rendered selectively in the animation, and discontinuity can be observed in the rendered animation. For instance, if a dragging input passes through at least three points of a path, the image manipulation application may only render the image at the first and third points, causing the image to “jump” between the first and third points rather than being rendering smoothly along the path. Other solutions use a copy of the image at a specific resolution lower than the original resolution of the image during the animation rendering. Although this type of solutions is able to reduce the lag between the rendered image and the position of the input device, the rendered images during the animation are fixed at the specific low resolution even if the computing device is capable of rendering a higher resolution image for certain renditions, resulting in a poor visual quality of the rendered images. 
     SUMMARY 
     Certain embodiments involve dynamically adjusting image resolution for animating or otherwise rendering changes to image content within an image manipulation. In one example, an image manipulation application receives, through a user interface, an input to manipulate an image. The image is displayed at a target resolution that is suitable for the current settings of the user interface. The image manipulation application manipulates the image based on the input and generates multiple renditions of the image to show an animation of the image manipulation. To determine the resolution of the image for the next rendition, the image manipulation application determines a normalized number of tracker events between two consecutive renditions of the image. Based on the determined normalized number of tracker events, the image manipulation application selects a version of the image from a set of versions of the image that have different resolutions of the image including the target resolution. In one example, the selected version of the image has a lower resolution than the target resolution. The image manipulation application manipulates the selected version of the image based on the input and generates an updated image for display in the user interface in the next rendition of the image. 
     These illustrative embodiments are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided there. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features, embodiments, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings. 
         FIG. 1  depicts an example of a computing environment for using dynamic image resolution adjustment for rendering changes to image content in an image manipulation application, according to certain embodiments of the present disclosure. 
         FIG. 2  depicts an example of a process for performing dynamic image resolution adjustment, according to certain embodiments of the present disclosure. 
         FIG. 3  depicts an example of an animation of a rotation manipulation on an image, according to certain embodiments of the present disclosure. 
         FIG. 4  depicts an example of image renditions and tracker events captured between consecutive image renditions, according to certain embodiments of the present disclosure. 
         FIG. 5  depicts an example of a pyramid of images at various resolutions, in accordance with certain embodiments of the present disclosure. 
         FIG. 6  depicts an example of a process for selecting a version of the image from an image pyramid, according to certain embodiments of the present disclosure. 
         FIG. 7  depicts an example of an animation of a rotation manipulation with dynamic image resolution adjustment in accordance with certain embodiments of the present disclosure. 
         FIG. 8  depicts an example of a computing system that executes an image manipulation application for performing certain embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure involves dynamically adjusting image resolution for animating or otherwise rendering changes to image content within an image manipulation. As discussed above, existing image manipulation methods often animate edits to image content with insufficient visual quality, where noticeable lags may occur between the movement of a user input and a rendering of the corresponding image manipulation. Certain embodiments described herein address these limitations by dynamically adjusting the resolution of the image from rendition to rendition based on an input for manipulating the image. For instance, an image manipulation application could increase or decrease the resolution of the image being manipulated for a next rendition based on previous renditions and activities of the input device occurred between two consecutive renditions. If a large number of input activities have occurred between the last two renditions, the image manipulation application will reduce the resolution of the image to speed up the rendering process so that a sufficient number of rendered images can be generated to reflect the input activities. Conversely, if a small number of input activities have occurred between the last two renditions, the image manipulation application will determine that current renditions are sufficient for providing the animation. The image manipulation application can increase the resolution of the image for the next rendition to provide a high visual quality of the rendered image. As a result, the image manipulation application can provide a smooth animation of the manipulation without unnecessarily sacrificing the visual quality of the rendered images. 
     The following non-limiting example is provided to introduce certain embodiments. In this example, an image manipulation application is used to manipulate an image according to an input and to generate a series of images based on the input to provide an animation of the manipulation. The image manipulation application receives an input from an input device indicating a request to manipulate an image displayed in a user interface at a target resolution. As used herein, the term “target resolution” of an image refers to a lowest resolution at which the image can be displayed at current settings without introducing visual artifacts, such as pixelations. The image manipulation application manipulates the image and generates various rendered images based on changes in the input. For instance, if the input is a dragging motion along an arc path, which could indicate a rotation operation to be applied the image, the image manipulation application will render and display an image rotated by a corresponding number of degrees as the input moves along the arc path. 
     The image manipulation application monitors the activities of the input device between two consecutive renditions of the image. In some embodiments, the activities of the input device is measured based on the number of tracker events generated by the input device. As used herein, the term “tracker event” refers to an event notifying that movement or other changes of an input device have been detected. For instance, a mouse moving from one location to another location involves multiple small movements of the mouse, and an image manipulation application will therefore generate multiple tracker events corresponding to these small movements. The number of the tracker events is an indication of the number of rendered images to be generated. A higher number of tracker events can indicate a constantly changing input, which could require renditions of an image to keep the animation of the corresponding image manipulation smooth and continuous. On the other hand, a lower number of the tracker events can mean that the input changes slowly and thus fewer image renditions would be sufficient to provide a smooth animation. 
     Continuing with this example, a high number of tracker events between two consecutive renditions of the image can indicate that the current renditions of the image are insufficient to prevent lags or discontinuities when animating an image manipulation application corresponding to a user input. To increase the performance of a rendering process, the image manipulation application selects a version of the image with a lower resolution for rendering. The image manipulation application selects the image version from an image pyramid containing multiple versions of the image at different resolutions, where the selected image version has a lower resolution than a current version used for rendering operations (e.g., a version that has been selected when the user input begins). In the next rendition of the image, the image manipulation application manipulates the selected image to generate a rendered image for display. 
     The image manipulation application continues to monitor the number of tracker events between two consecutive renditions of the image. If the number of tracker events between two consecutive renditions of the image is still high, the image manipulation application accesses the image pyramid and selects a version of the image having a lower resolution than the current version of the image. Conversely, if the number of tracker events between two consecutive renditions of the image is low, the image manipulation application selects a version of the image having a resolution higher than the current version of the image to provide a higher visual quality of the rendered image. The above process continues as the manipulation of the image is performed. The image manipulation application can return to a target resolution for rendering the image if the manipulation is complete (e.g., upon the release of a mouse button used for a dragging input). 
     As described herein, certain embodiments provide improvements in image processing by dynamically adjusting the resolution of an image rendered for animation of image manipulation. The dynamic resolution adjustment improves the visual quality of the animation by providing a smooth animation of the manipulation without unnecessarily reducing the resolution of rendered images. The dynamic nature of the resolution adjustment eliminates the need of operating on a high resolution image throughout the animation process. Thus, embodiments described herein improve the image manipulation by reducing the consumption of computing resources, such as CPU times and memory spaces. 
     As used herein, the term “image” refers to a photo, a picture, a digital painting, a computer-generated graphic, or any other artifact that depicts visual perception. As used herein, “rendering an image” or “manipulating an image” refers to the process of performing operations on an image based on one or more commands and generating an output image for display. For example, rendering or manipulating an image based on a rotation command includes rotating the layers of the image affected by the rotation if the image is represented by multiple layers, rotating titles of the image if the image containing multiple titles, or some combination thereof. The rendering or manipulating further includes compo siting the multiple layers or tiles of the image into a single output image for display. The performance of the rendering or manipulating process is referred to herein as “image rendition” or “rendition.” The output image generated by the rendering or manipulating process is referred to herein as the “rendered image.” 
     Example Operating Environment for Image Manipulation with Dynamic Resolution Adjustment 
     Referring now to the drawings,  FIG. 1  depicts an example of a computing environment  100  for using an image manipulation application  102  to perform image manipulation based on an manipulation input  134 . The computing environment  100  includes one or more processing devices that execute an image manipulation application  102 , an input device  104  for providing commands to the image manipulation application  102 , and a display device  132  for displaying a user interface for receiving commands from the input device  104  and for displaying a rendered image  130  generated by the image manipulation application  102  based on the commands. 
     The image manipulation application  102  includes an input processing engine  112  for receiving or detecting the manipulation input  134  generated by the input device  104  and an image rendering engine  116  for generating rendered images based on the manipulation input  134  and for providing each rendered image  130  to the display device  132  for display. The image manipulation application  102  further includes a resolution adjustment engine  114  for adjusting the resolution of the image being processed by the image rendering engine  116  so that a smooth and high visual quality animation of the manipulation can be generated. 
     The input processing engine  112  detects the manipulation input  134  from an input device  104  that is operated by a user by detecting tracker events  136  generated by the input device  104 . Examples of an input device  104  include a touch device  108 , such as a touchscreen or a touchpad, a mouse  106 , a stylus  110 , etc. Examples of tracker events include mouse clicks, dragging inputs, taps, pinches, spreads, rotates, swipe, applying pressure with the stylus, changing angle of the stylus, etc. 
     The input processing engine  112  interprets the detected manipulation input  134  and generates manipulation commands  118  so that the image rendering engine  116  can manipulate the image according to the manipulation input  134 . In one example, the input processing engine  112  interprets the manipulation input  134  based on the type of the tracker events  136  contained in the manipulation input  134 . For example, if the tracker events  136  include a dragging event of the mouse  106  that drags an image displayed in a user interface from one position to another position, the input processing engine  112  will interpret that the manipulation input  134  indicates a shifting operation on the image and the shifting parameters would be the difference of the original position of the image and the new position indicated by the position of the mouse  106 . The input processing engine  112  generates the manipulation commands  118  based on this interpretation and sends the commands to the image rendering engine  116  for execution. In another example, if the tracker events  136  include pressure application of the stylus  110  on an image displayed in the user interface, the input processing engine  112  will interpret that the manipulation input  134  indicates a blurring operation on the image and the degree of blurring is determined by the amount of the pressure applied by the stylus  110 . The input processing engine  112  generates the manipulation commands  118  for blurring for the image rendering engine  116 . 
     In some cases, the manipulation input  134  is generated by an input device  104  based on a single action of the user, such as a mouse click. In those cases, the tracker events  136  can include a small number of tracker events. In the example of a mouse click, the tracker events  136  include a mouse down event indicating the mouse is pressed down, and a mouse up event indicating the mouse is released. In other cases, however, the manipulation input  134  is generated by an input device  104  through a series of actions of the user, such as a mouse dragging, a continuous pressure application of a stylus  110 , a pinch or spread using two fingers on the touch device  108 , etc. The tracker events  136  for this type of manipulation input  134  would include multiple events, such as a mouse down event, multiple mouse moves event and a mouse up event, or a stylus down event, multiple stylus pressure change events, and a stylus up event. If the manipulation input  134  involves a series of actions, the input processing engine  112  will interpret the manipulation input  134  as new tracker events  136  are generated by the input device  104  until the series of actions are complete, such as the mouse is released or the stylus is lifted. The input processing engine  112  can send a message to the image rendering engine  116  indicating the completion of the manipulation. 
     The image rendering engine  116  receives the manipulation commands  118  from the input processing engine  112  and performs the manipulation on the rendered image  130  accordingly. In order to provide an animation of the image manipulation process, the image rendering engine  116  generates multiple rendered images for the manipulation process that correspond to the series of actions indicated in the manipulation input  134 . Detailed examples of generating multiple rendered images for a manipulation process are described herein with respect to  FIGS. 3 and 7 . 
     To perform dynamic resolution adjustment, the resolution adjustment engine  114  obtains rendition information  124  from the image rendering engine  116 . The rendition information  124  includes information associated with each rendition of the image, such as a time stamp associated with the rendition, the resolution of the image used in the rendition, and so on. In addition, the resolution adjustment engine  114  obtains the number of tracker events  140  between two consecutive renditions of the image from the input processing engine  112 . 
     As discussed above, the number of tracker events  140  between two consecutive renditions of the image can be utilized as an indication for the activeness of the manipulation input  134 . For the same type of input device  104 , a higher number of tracker events  140  between two consecutive renditions of the image can mean that more actions are involved in the manipulation input  134  and the current frequency of image renditions might not be enough to provide a smooth animation for the manipulation. As such, the resolution adjustment engine  114  determines a version of the image with a lower resolution than the current version of the image is to be used so that the speed of the rendition can be increased and more rendered images can be generated to reflect the actions in the manipulation input  134 . If the number of tracker events  140  between two consecutive renditions of the image is low, then the resolution adjustment engine  114  can determine that the current frequency of rendition is high and the resolution of the image can be increased to improve the visual quality of the rendered image. 
     In some embodiments, the resolution adjustment engine  114  determines whether the number of tracker events is high or low by comparing the number of tracker events with threshold numbers of tracker events. In addition, the number of tracker events  140  obtained from the input processing engine  112  might contain noises caused by, for example, false detections of the tracker events  136 . The resolution adjustment engine  114  can reduce the impact of these noises by converting the number of tracker events  140  in a logarithmic scale, and by calculating a normalized number of tracker events  122  to including the number of tracker events  140  between the current two consecutive renditions and past consecutive renditions of the image. The normalized number of tracker events  122  is used for comparison with the threshold numbers of tracker events to determine whether to adjust the resolution of the image. Detailed examples of calculating and comparing the normalized number of tracker events  122  with threshold numbers of tracker events are described herein with respect to  FIGS. 4 and 6 . 
     The decision of increasing or decreasing the resolution of the image is included in resolution adjustment  126  and sent to the image rendering engine  116 . In some embodiments, the resolution adjustment engine  114  determines the resolution adjustment  126  by referencing to an image pyramid  120 . The image pyramid  120  includes multiple versions of the image being manipulated, each version corresponding to a different resolution of the image. The multiple versions of the images are ordered according to their resolutions, for example, from the highest resolution to the lowest resolution with the highest resolution as level  1  and the lowest resolution as level N. 
     If the resolution adjustment engine  114  decides to lower the resolution of the image, the resolution adjustment engine  114  can instruct the image rendering engine  116 , in the resolution adjustment  126 , to move down in the image pyramid  120  to retrieve a version of the image having a lower resolution. Similarly, if the resolution adjustment engine  114  decides to increase the resolution of the image, the resolution adjustment engine  114  can instruct the image rendering engine  116  to move up in the image pyramid  120  to retrieve a version of the image having a higher resolution. In some embodiments, in order to prevent abrupt changes in the rendered image  130 , each adjustment of the resolution of the image cannot be more than one level. Detailed examples of the image pyramid  120  are described herein with respect to  FIG. 5 . 
     Based on the resolution adjustment  126 , the image rendering engine  116  accesses the image pyramid  120  and retrieves the version of the image in the corresponding level as an working image  128 . The image rendering engine  116  performs the operations on the working image  128  according to the manipulation commands  118 , thereby generating an updated rendered image  130  in the next image rendition. The image rendering engine  116  provides the updated rendered image  130  to the display device  132  for display. Additional details regarding the dynamic adjustment of image resolution are described herein with respect to  FIGS. 2-7 . 
     Examples of Computer-Implemented Operations for Dynamic Resolution Adjustment 
       FIG. 2  depicts an example of a process  200  for performing dynamic image resolution adjustment, according to certain embodiments of the present disclosure. One or more computing devices (e.g., the computing environment  100 ) implement operations depicted in  FIG. 2  by executing suitable program code (e.g., the image manipulation application  102 ). For illustrative purposes, the process  200  is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. 
     At block  202 , the process  200  involves receiving an input through a user interface presented by an image manipulation application  102  to manipulate an image displayed at a target resolution in the user interface. One or more computing devices execute program code from the image manipulation application  102  to implement block  202 . For instance, the image manipulation application  102  monitors, detects, receives, or otherwise accesses the manipulation input  134  from one or more input devices  104 . The input device  104  can include a mouse  106 , a touch device  108 , a stylus  110 , or other devices. The manipulation input  134  includes one or more actions that are described by tracker events  136 , such as mouse clicks, dragging inputs, taps, pinches, spreads, rotates, swipe, applying pressure with a stylus, changing angle of a stylus, etc. In some embodiments, an input processing engine  112  detects and interprets the tracker events  136  to generate manipulation commands  118  that include the type of the operations involved in the manipulation and the parameters for the operations. 
     At block  204 , the process  200  involves manipulating the image based on the manipulation commands  118  generated according to the manipulation input  134 . One or more computing devices execute program code from the image manipulation application  102  to implement block  204 . For instance, an image rendering engine  116  of the image manipulation application  102  accesses the manipulation commands  118  and performs operations specified in the manipulation commands  118  on the image, such as rotation, shifting, scaling, blurring, and so on. The image rendering engine  116  generates multiple rendered images corresponding to multiple manipulation commands  118  received from the input processing engine  112 . An animation of the manipulation process is provided by displaying the rendered images  130  on a display device  132  along the movement or other changes of the input device  104 . 
       FIG. 3  depicts an example of an animation of a rotation manipulation on an image that includes multiple image renditions  302 A- 302 E (which may be referred to herein individually as an image rendition  302  or collectively as the image renditions  302 ). The rotation manipulation might be based on a manipulation input  134  containing a mouse  106  moving along a half circle clockwise to indicate rotating the image by 180 degrees. At rendition  302 A, the mouse is at its starting location and thus image is at its original state without any rotation. As the mouse starts to move, the image rendering engine  116  starts to generate image renditions  302 . As shown in  FIG. 3 , the image rendering engine  116  generates an image rendition  302 B rotated by 45 degrees corresponding to the mouse moving to a position between 1 o&#39; clock and 2 o&#39; clock, and an image rendition  302 C rotated by 90 degrees corresponding to the mouse moving to the 3 o&#39;clock position. Similarly, an image renditions  302 D and an image renditions  302 E are generated for the mouse moving to a position between 4 o&#39; clock and 5 o&#39; clock, and a position near 6 o&#39; clock, respectively. The rendered image in each image rendition  302  is displayed on the display device  132  one by one as the mouse moves to provide an animation of the rotation manipulation of the image. 
     Referring back to  FIG. 2 , at block  206 , the process  200  involves determining the normalized number of tracker events  122  between two consecutive renditions of the image. One or more computing devices execute program code from the image manipulation application  102  to implement block  206 . In some cases, the resolution adjustment engine  114  receives the rendition information  124  from the image rendering engine  116  to determine the current two consecutive renditions of the image. The resolution adjustment engine  114  also obtains the number of tracker events  140  between two consecutive renditions of the image from the input processing engine  112 . 
       FIG. 4  depicts an example of image renditions and tracker events captured between consecutive image renditions. As shown in  FIG. 4 , between two consecutive renditions  406  of the image, a number of tracker events  402  can occur and be captured by the input processing engine  112 . The resolution adjustment engine  114  tracks each image rendition  406  based on the rendition information  124  and obtains the number of tracker events  140  between two consecutive renditions of the image from the input processing engine  112 . In some embodiments, the resolution adjustment engine  114  calculates a normalized number of tracker events  122  by incorporating the number of tracker events  140  between past consecutive renditions of the image. Denoting the number of tracker events  140  between rendition i−1 and rendition i as M i , and assuming the current rendition is rendition K, the normalized number of tracker events  122 , denoted as {tilde over (M)} K , can be calculated as 
                       M   ~     K     =       α   ⁢           ⁢     1     K   -   1       ⁢       ∑     i   =   1       K   -   1       ⁢     M   i         +       (     1   -   α     )     ⁢       M   K     .                 (   1   )               
where α is a parameter indicating a weight distribution between the number of tracker events between the current two consecutive renditions and the number of tracker events between past consecutive renditions of the image. For example, the resolution adjustment engine  114  can assign a value of 0.7 to α to apply more weights to the past number of tracker events than the current number of tracker events. Alternatively, the resolution adjustment engine  114  can assign more weights to the current number of tracker events by choose a value less than 0.5 for α.
 
     By incorporating the past number of tracker events, the resolution adjustment engine  114  can reduce the impact of the noises in the current number of tracker events, thereby providing more stable and reliable resolution adjustment. To further reduce the impact of the noises in the number of tracker events  140 , especially, the spikes in the number of tracker events  140 , the resolution adjustment engine  114  can calculate the normalized number of tracker events  122  on a logarithmic scale. In this case, Equation (1) becomes: 
     
       
         
           
             
               
                 
                   
                     
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     The logarithmic operator scales down the value of M i . As a result, a spike in M i  would not cause a large change in the calculated normalized number of tracker events {tilde over (M)} K . The logarithmic operation can be performed with respect to any bases, such as 2, 10, or the natural base e. 
     Depending on the type of input device, the normalized number of tracker events can be adjusted to accurately reflect the activity of the input device. For example, a stylus  110  can generate much more tracker events than a mouse  106 . One of the reasons is that in addition to the movement of the stylus, a pressure change or an angle change of the stylus can cause the generation of the tracker events  136 . To reduce the impact of these additional tracker events, for a stylus input device, the number of tracker events  140  is reduced, instead of being increased, if a tracker event is received at the same point until the number of tracker events  140  becomes 0. This helps to reduce the number of tracker events, thus making it more likely for a higher resolution image to be selected. This is beneficial because if the stylus is stationary, it is more likely that the user wants to have a high resolution image to be displayed so that the manipulation of the image can be performed more precisely. 
     In another example, a user input might include several mouse inputs each including a mouse down event, several mouse drag events and a mouse up event. For example, a user uses a mouse to continuously adjusting the position of an image in a short period of time by moving the image to various different locations through several mouse-down-mouse-up cycles. In such a scenario, calculating the normalized number of tracker events  122  for a subsequent mouse down event can be adjusted to take into account the number of tracker events calculated in previous mouse down/mouse up cycle. For example, for a subsequent mouse down event, in the calculation of the normalized number of tracker events  122 , an averaged number of tracker events from previous mouse-down/mouse-up cycles is used in the first term in Equation (2) instead of setting it to zero. Because the image, device, and zoom level remain the same for the current cycle and previous cycles, by taking into account the number of tracker events in previous cycles, the proper image resolution that is suitable for the current setting can be found faster. 
     Referring back to  FIG. 2 , at block  208 , the process  200  involves selecting a version of the image for rendering based on the determined normalized number of tracker events  122 . One or more computing devices execute program code from the image manipulation application  102  to implement block  208 . In some embodiments, the resolution adjustment engine  114  selects the version of the image from an image pyramid  120 . The image pyramid  120  includes multiple versions of the image being manipulated, each version corresponding to a different resolution of the image. 
       FIG. 5  depicts an example of an image pyramid  120  that includes five versions of the image at five levels of resolution ordered from the highest to the lowest. In some embodiments, the resolution at level i is half of that of level i−1 in both dimensions of the image. For example, if an image at level i−1 has a resolution of N x ×N y , the resolution of the level i image has a resolution of N x /2×N y /2. In other words, the computational complexity of processing a level i image is one quarter of that of processing a level i−1 image. As a result, if the resolution adjustment engine  114  selects a version of the image from a level that is below the current level in the image pyramid  120 , the rendering speed of the image can be significantly increased. Note that the target resolution of the image as discussed above is not necessarily the highest resolution of the image, although manipulation of the image, such as zooming in, may change the target resolution to be the highest resolution of the image. 
     Referring back to  FIG. 2 , in some implementations, the resolution adjustment engine  114  determines the version of the image to be selected by comparing the normalized number of tracker events  122  with two threshold numbers of tracker events: an upper threshold and a lower threshold. If the normalized number of tracker events is higher than the upper threshold number of tracker events, the resolution adjustment engine  114  determines that a version of the image with a lower resolution than the current version should be selected, i.e. moving down in the image pyramid  120 . If the normalized number of tracker events is lower than the lower threshold number of tracker events, a version of the image having a higher resolution than the current version will be selected, i.e. moving up in the image pyramid  120 . Otherwise, that is, the normalized number of tracker events is higher than the lower threshold and lower than the higher threshold, the resolution of the image remains unchanged. 
     In some embodiments, the resolution adjustment engine  114  also take into account other considerations in selecting the version of the image. For example, in order to prevent the visual quality of the rendered image  130  from significantly deviating from the target resolution, the resolution adjustment engine  114  sets a lowest resolution permitted for the image for rendering and the selected version of the image cannot have a resolution lower than that resolution. Further, the resolution adjustment engine  114  can limit the resolution adjustment in each rendition to a certain amount in order to prevent abrupt changes in the visual quality of the rendered image  130 . Detailed examples of the selecting a version of the image for rendering are described herein with respect to  FIG. 6 . 
     At block  210 , the process  200  involves generating a rendered image  130  by manipulating the selected version of the image. One or more computing devices execute program code from the image manipulation application  102  to implement block  210 . For instance, the image rendering engine  116  can retrieve the selected version of the image from the image pyramid as the working image and apply the manipulation operations on the working image based on the manipulation commands  118  generated from the manipulation input  134 . The image rendering engine  116  provides the rendered image  130  to the display device  132  for display. 
       FIG. 6  depicts an example of a process  600  for selecting a version of the image from an image pyramid  120 . One or more computing devices (e.g., the computing environment  100 ) implement operations depicted in  FIG. 6  by executing suitable program code (e.g., the image manipulation application  102 ). For illustrative purposes, the process  600  is described with reference to certain examples depicted in the figures. Other implementations, however, are possible. 
     At block  602 , the process  600  involves comparing the normalized number of tracker events  122  calculated at block  206  of the process  200  with the upper and lower threshold numbers of tracker events. One or more computing devices execute program code from the image manipulation application  102  to implement block  602 . For instance, the resolution adjustment engine  114  or other modules in the image manipulation application  102  determines the upper threshold and the lower threshold based on, for example, past experience of resolution adjustment, experiment results, or other factors. The resolution adjustment engine  114  also determines the value of the thresholds by considering how the normalized number of tracker events  122  is calculated at block  206  in process  200 . For example, if the resolution adjustment engine  114  calculates the normalized number of tracker events  122  without using the logarithmic scale, then the thresholds are determined at a scale that match the normalized number of tracker events  122 . If, at block  206 , the resolution adjustment engine  114  determines the normalized number of tracker events  122  on a logarithmic scale, the thresholds should be also determined on the same logarithmic scale. 
     The resolution adjustment engine  114  further determines the values of the thresholds based on the type of the input device  104 . For example, as discussed above, a stylus  110  can generate much more tracker events than a mouse  106 . In a scenario where the manipulation is indicated only by the movement of the stylus, pressure changes and angle changes of the stylus can cause the number of tracker events  140  for the stylus to be much higher than that of a mouse if the mouse was used as an input device. Likewise, the tracker events  136  generated for a touch device  108  might also be different from the tracker events of a mouse or a stylus. The resolution adjustment engine  114  thus takes this into consideration in determining the values of the upper and lower thresholds. 
     At block  604 , the resolution adjustment engine  114  determines whether the normalized number of tracker events  122  is greater than the upper threshold number of tracker events. If the normalized number of tracker events  122  is not greater than the upper threshold number of tracker events, the resolution adjustment engine  114  determines, at block  606 , that whether the rendered image in the current rendition is at the target resolution. If the rendered image in the current rendition is at the target resolution, the resolution adjustment engine  114  determines, at block  624 , that the resolution of the image remains the same. If the rendered image in the current rendition is not at the target resolution, the resolution adjustment engine  114  determines, at block  607 , whether the normalized number of tracker events  122  is lower than the lower threshold. If the normalized number of tracker events  122  is not lower than the lower threshold, the resolution adjustment engine  114  determines, at block  624 , that the resolution of the image remains unchanged. If it is determined at block  607  that the normalized number of tracker events  122  is lower than the lower threshold, the resolution adjustment engine  114  determines, at block  608 , to increase the resolution of the image by selecting a version of the image with a higher resolution. For example, as discussed above, the resolution adjustment engine  114  can select the version of image by moving up in the image pyramid  120 . In order to avoid abrupt change in visual quality of the rendered image, the resolution adjustment engine  114  can select a version of the image by moving up in the image pyramid  120  by one or two levels, i.e. selecting a version of the image that is one or two levels up from the current version of the image. 
     If, at block  604 , the resolution adjustment engine  114  determines that the normalized number of tracker events  122  is greater than the upper threshold number of tracker events, the resolution adjustment engine  114  compares the current image resolution with a lowest resolution permitted for the image, at block  610 . The lowest resolution is set so that the visual quality of the rendered image does not significantly deviate from the target resolution. For example, the resolution adjustment engine  114  may determine the lowest resolution to be a level that is three levels down from the target resolution. In the example shown in  FIG. 5 , the target resolution is at level  2 , and the resolution adjustment engine  114  may determine that the lowest resolution is at level  5 . In other words, no versions of the images below level  5  can be selected for rendering. 
     If, at block  610 , the resolution adjustment engine  114  determines that the current resolution of the image has not reached the lowest resolution, the resolution adjustment engine  114  determines, at block  612 , to decrease the resolution of the image by selecting a version of the image with a lower resolution. For example, the resolution adjustment engine  114  can select the version of image by moving down in the image pyramid  120  by one or two levels. If, at block  610 , the resolution adjustment engine  114  determines that the current resolution of the image has reached the lowest resolution, the resolution adjustment engine  114  further determines, at block  614 , whether the image in the last T consecutive renditions remain at the lowest resolution. The rendered image remaining at the lowest resolution for the past T renditions can indicate that the upper threshold number of tracker events is too low, resulting in the resolution of the image being maintained rather than being dynamically adjusted. To avoid this problem, resolution adjustment engine  114  can increase the value of the upper threshold at block  616 . Here, T is a value that can be selected by the resolution adjustment engine  114  to determine when the threshold number of tracker events should be updated. If the rendered image does not remain at the lowest resolution for the past T renditions, the resolution adjustment engine  114  will determine, at block  626 , that the current image still use the lowest resolution. 
     In one implementation, the lower threshold is adjusted in a manner similar to the upper threshold, that is, the lower threshold is increased by the same amount or by the same percentage if the upper threshold is increased. For example, the resolution adjustment engine  114  can determine if the image remains at the target resolution for the past several consecutive renditions. If so, the resolution adjustment engine  114  will determine that the lower threshold, as well as the upper threshold. The adjustment of the threshold can be made based on the resolution of the past several consecutive renditions as described above. It can also be made based on the percentage of the image remaining at the lower resolution or the target resolution. For example, if the image remains as the lowest resolution for 95% of the time, the upper and lower thresholds will be increased. Likewise, if the image remains at the target resolution for 95% of the time, the upper and lower thresholds will be decreased. 
     At block  618 , the resolution adjustment engine  114  determines whether the manipulation has completed or not. The resolution adjustment engine  114  can determine the completion of the manipulation by receiving a message from the input processing engine  112  that an event indicating the end of the manipulation input  134  has been generated, such as a mouse up event, a finger lift event, or a stylus life event. If the manipulation has not completed, the resolution adjustment engine  114  selects the image with the resolution determined as described above. If the resolution adjustment engine  114  determines that the manipulation has completed, the resolution adjustment engine  114  selects, at block  622 , the image with the target resolution. 
       FIG. 7  depicts a simplified example of an animation of a rotation manipulation with dynamic image resolution adjustment. As shown in  FIG. 7 , multiple renditions are performed for the rotation manipulation at various rotation degrees thereby providing a smooth animation effect. Further, the rendered images at different renditions have different resolutions, indicated by the level number of the pyramid associated with the image. The resolution of the rendered image increases or decreases from rendition to rendition due to the dynamic nature of the resolution adjustment. 
     Computing System Example for Implementing Digital Overpainting 
     Any suitable computing system or group of computing systems can be used for performing the operations described herein. For example,  FIG. 8  depicts an example of a computing system  800  that can implement the computing environment of  FIG. 1 . In some embodiments, the computing system  800  includes a processing device  802  that executes the image manipulation application  102 , a memory that stores various data computed or used by the image manipulation application  102 , an input device  104  (e.g., a mouse, a stylus, a touchpad, a touchscreen, etc.), and a display device  132  that displays graphical content generated by the image manipulation application  102 . For illustrative purposes,  FIG. 8  depicts a single computing system on which the image manipulation application  102  is executed, the image pyramid  120  is stored, and the input device  104  and display device  132  are present. But these applications, datasets, and devices can be stored or included across different computing systems having devices similar to the devices depicted in  FIG. 8 . 
     The depicted example of a computing system  800  includes a processing device  802  communicatively coupled to one or more memory devices  804 . The processing device  802  executes computer-executable program code stored in a memory device  804 , accesses information stored in the memory device  804 , or both. Examples of the processing device  802  include a microprocessor, an application-specific integrated circuit (“ASIC”), a field-programmable gate array (“FPGA”), or any other suitable processing device. The processing device  802  can include any number of processing devices, including a single processing device. 
     The memory device  804  includes any suitable non-transitory computer-readable medium for storing data, program code, or both. A computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, a memory chip, a ROM, a RAM, an ASIC, optical storage, magnetic tape or other magnetic storage, or any other medium from which a processing device can read instructions. The instructions may include processor-specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript. 
     The computing system  800  may also include a number of external or internal devices, such as an input device  104 , a display device  132 , or other input or output devices. For example, the computing system  800  is shown with one or more input/output (“I/O”) interfaces  808 . An I/O interface  808  can receive input from input devices or provide output to output devices. One or more buses  806  are also included in the computing system  800 . The buses  806  communicatively couples one or more components of a respective one of the computing system  800 . 
     The computing system  800  executes program code that configures the processing device  802  to perform one or more of the operations described herein. The program code includes, for example, the image manipulation application  102  or other suitable applications that perform one or more operations described herein. The program code may be resident in the memory device  804  or any suitable computer-readable medium and may be executed by the processing device  802  or any other suitable processor. In some embodiments, all modules in the image manipulation application  102  (e.g., the input processing engine  112 , the resolution adjustment engine  114 , the image rendering engine  116 , etc.) are stored in the memory device  804 , as depicted in  FIG. 8 . In additional or alternative embodiments, one or more of these modules from the image manipulation application  102  are stored in different memory devices of different computing systems. 
     In some embodiments, the computing system  800  also includes a network interface device  810 . The network interface device  810  includes any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks. Non-limiting examples of the network interface device  810  include an Ethernet network adapter, a modem, and/or the like. The computing system  800  is able to communicate with one or more other computing devices (e.g., a computing device that receives inputs for image manipulation application  102  or displays outputs of the image manipulation application  102 ) via a data network using the network interface device  810 . 
     An input device  104  can include any device or group of devices suitable for receiving visual, auditory, or other suitable input that controls or affects the operations of the processing device  802 . Non-limiting examples of the input device  104  include a touchscreen, stylus, a mouse, a keyboard, a microphone, a separate mobile computing device, etc. A display device  132  can include any device or group of devices suitable for providing visual, auditory, or other suitable sensory output. Non-limiting examples of the display device  132  include a touchscreen, a monitor, a separate mobile computing device, etc. 
     Although  FIG. 8  depicts the input device  104  and the display device  132  as being local to the computing device that executes the image manipulation application  102 , other implementations are possible. For instance, in some embodiments, one or more of the input device  104  and the display device  132  can include a remote client-computing device that communicates with the computing system  800  via the network interface device  810  using one or more data networks described herein. 
     General Considerations 
     Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provide a result conditioned on one or more inputs. Suitable computing devices include multi-purpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device. 
     Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting. 
     While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude the inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.