Scene-based edit suggestions for videos

Embodiments are disclosed for determining scene-based editing recommendations for video content. A method of determining scene-based editing recommendations for video content includes receiving an input video comprising video content, dividing the input video into the plurality of scenes based on the video content, identifying a representative frame for each scene, determining a plurality of editing settings for each representative frame, determining editing settings for each scene based on an effectiveness score, and generating an output video using the input video and the editing settings for each scene.

BACKGROUND

As image capture and computing device technology has improved, increasingly powerful video capture and editing tools have become available to professional users and consumers. This has led to a boom of content creation. Users can now quickly capture and edit videos and then share the video with others (e.g., via social media or other channels). Video editing tools provide a variety editing functions that let the user manipulate their video content, such as color grading, lens corrections, white balance, saturation, brightness, etc. Additionally, such tools provide various transforms, corrections, filters, etc. which the user can choose to apply to their video. Editing video using these tools typically involves a manual process of trial and error to identify the best edits to be made at the appropriate times in the video content. This results in a generally cumbersome workflow which also requires specialized knowledge or experience to ensure that the best edits are applied to video content.

SUMMARY

Introduced here are techniques/technologies that provide scene-based editing recommendations for video content. In particular, in one or more embodiments, the disclosed systems and methods comprise a video editing recommendation system that receives a video to be edited. The video is automatically divided into scenes which are each edited separately. For example, scenes are identified based on their visual characteristics. Scenes with different visual characteristics likely cannot be edited using the same editing settings while expecting the same quality result. Accordingly, recommended editing settings are then determined for each scene separately. For example, a machine learning model is used to identify a representative frame for each scene which most accurately represents the visual characteristics of the scene as a whole.

Using the representative frames, the performance of the presets is analyzed. For example, an aesthetics machine learning model determines a score for each frame before and after applying a preset. These scores are used to determine how effective the preset is across the entire scene. The presets that perform well are retained while those performing poorly are discarded. The resulting list of recommended presets is then presented to the user to select one preset per scene. Once selected, each scene is separately edited, and the edited scenes are then combined to generate an edited output scene. This improves the visual quality of the output video without requiring the user to manually divide the input video into scenes and manually edit each scene separately.

Additional features and advantages of exemplary embodiments of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such exemplary embodiments.

DETAILED DESCRIPTION

One or more embodiments of the present disclosure include a video editing system which provides scene-based editing recommendations based on the content of the scenes. Conventional video editing system enable a user to specify editing settings for a video file. However, it is difficult to find editing settings that work for all frames in the video. Typically, the user is shown one frame, and then the same edited frame once their editing settings have been applied. If the user finds these changes acceptable, then the same settings are applied to the entire video. However, while these settings may be effective for the shown frame, they do not necessarily improve the appearance of other frames. For example, changing the exposure value to improve the appearance of one frame can result in other frames being over- or under-exposed. Similarly, videos often include multiple scenes. Different scenes are often shot at different locations, under different lighting conditions, etc. As a result, the edits that improve the appearance of one scene do not necessarily improve the appearance of different scenes. Conventional video editing systems do not enable scene-specific editing. Instead, they require the user to manually divide a video into multiple scenes, apply separate edits to each scene, and then recombine the edited scenes to make the edited video. This is both time consuming and requires specialized skills by the editor to ensure that an acceptable edited video is produced.

Embodiments address the deficiencies in conventional techniques by automatically dividing a video into its component scenes and providing scene-specific editing recommendations. For example, scenes are identified by changes in the visual characteristics of the frames of the video. Such changes often indicate a change in location, lighting, or other characteristics which make applying the same editing settings challenging. For instance, a video that starts in an exterior location and ends in an interior location will have different visual characteristics as the result of different lighting, different backgrounds, etc. Scenes are identified by the start and end frames of the scene (e.g., using timestamps). Unlike in conventional techniques, this is performed automatically, without requiring the user to manually identify scenes and generate separate video files.

With the scenes identified, scene-specific editing settings are then determined. For example, a machine learning model is used to analyze a representative frame from each scene which accurately represents the visual characteristics of the scene as a whole. Using the representative frame, the machine learning model identifies similar images and corresponding editing settings to be used for editing. Because this can result in a large number of sets of editing settings (also referred to as presets), the recommended editing settings are pruned based on their performance. For example, an effectiveness score is calculated for each preset for its scene. Those presets that perform well are presented to the user, while poor performing presets are discarded. Once the user has selected the preset to use for each scene, the scenes are edited and combined into an output video.

Term Definitions

As used herein, the term “video” or “digital video” refers to digital data representative of a sequence of visual images. In particular, a digital video includes a sequence of images which may include corresponding digital audio. For example, the term “digital video” includes, but is not limited to, digital files having one of the following file extensions: AVI, FLV, WMV, MOV, MP4. Similarly, as used herein, the term “frame” (or “frame of a digital video”) refers to a digital image from a digital video.

As used herein, the term “image” or “digital image” refers to a digital graphics file that when rendered displays one or more objects. In particular, the term “image” comprises a digital file that, when rendered, includes visual representations of one or more objects, such as a person. For example, the term “digital image” includes, but is not limited to, digital files with the following file extensions: JPG, TIFF, BMP, PNG, RAW, or PDF. Thus, a digital image includes digital data or a digital file for an image that is displayable via a graphical user interface of a display of a computing device. In some embodiments, an image refers to a frame of a digital video.

As used herein, the term “scene” refers to a series of consecutive frames of a digital video that share common visual characteristics, such as color composition. A digital video includes one or more scenes. Scenes are identified using scene detection techniques which identify variations in the content of frames of the video. As used herein, the term “representative frame” refers to a frame of a scene that best reflects the visual characteristics of the scene as a whole.

As used herein, the term “preset” refers to a set of editing settings that are applied to an image and/or video. The editing settings are used to manipulate attributes of the image and/or video. Examples attributes include exposure, contrast, white balance, highlights, clarity, etc. Presets are stored in a data structure which allows for the set of editing settings used for one image or video to be stored and applied to another image or video.

As used herein, the term “effectiveness score” refers to a numerical representation of the aesthetic improvements made to an image or video when applying a preset during editing. An effectiveness score is calculated for each frame of a scene based on an aesthetic score determined before the edits are applied and after the edits are applied. An average effectiveness score is computed for a given preset by combining the effectiveness score for each frame of the scene. This allows for the relative performance of each preset across a scene to be compared quantitatively.

As used herein, a “neural network” refers to a machine-learning model that can be tuned (e.g., trained) based on training input to approximate unknown functions. In particular, a neural network includes a model of interconnected digital neurons that communicate and learn to approximate complex functions and generate outputs based on a plurality of inputs provided to the model. For instance, the neural network includes one or more machine learning algorithms. In other words, a neural network is an algorithm that implements deep learning techniques, i.e., machine learning that utilizes a set of algorithms to attempt to model high-level abstractions in data.

FIG.1illustrates a diagram of a process of determining scene-based edit suggestions for video data in accordance with one or more embodiments. Embodiments include a video editing recommendation system100which uses machine learning to determine scene-based edit recommendations for video data. Accordingly, embodiments auto-segment an input video file into scenes before identifying recommended editing settings for each scene. In some embodiments, the user selects from multiple recommendations for all or some of the scenes. Once edited, the video editing recommendation system100merges the edited scenes to generate the edited output video.

As shown inFIG.1, the video editing recommendation system100receives an input video102at numeral1. In some embodiments, the video editing recommendation system100is implemented as part of a digital video editing system. The input video102is a digital video that is selected by a user (e.g., a content creator, editor, etc.) for editing. In some embodiments, the input video is locally stored with, or remotely accessible by, the video editing recommendation system. For example, in some embodiments, the user selects the video by browsing a file system to locate the input video, providing an identifier associated with the input video102(e.g., a URL, URI, etc.), etc.

In some embodiments, the user opens the input video in the video editing recommendation system100to begin editing. Alternatively, the user opens the input video in a video editing system in which the video editing recommendation system100is implemented. Once loaded, the user begins editing the input video using the video editing recommendation system100(e.g., by selecting a video editing recommendation icon in a user interface, or via other user interface interactions). When the input video is received by the video editing recommendation system100, it is processed by scene segmentation manager104, at numeral2. The scene segmentation manager identifies different scenes within the input video102. As discussed, different scenes often require different edits, as the edits made to one scene do not necessarily improve a different scene. The scene segmentation manager104performs temporal segmentation on the input video by detecting transitions between scenes.

In some embodiments, scene segmentation uses histogram difference to detect the transitions. Histogram difference is based on the color histogram differences between the two consecutive frames. In some embodiments, histogram difference is computed for each channel (e.g., RGB, or other color channels) and the difference is then summed up. Histogram difference is defined as:

where hiis a histogram with M bins of the ithframe of the digital video. If the HistDif[i] value is above a threshold, then the time stamp of the ithframe is marked as a separator between two scenes. The histogram differences technique assigns consecutive frames with similar colors to be part of the same scene. As a result, suggested edits are likely to be suitable for all frames of the scene as they share similar color composition. The timestamps are then used to identify a plurality of scenes106of the input video.

At numeral3, a frame selector108determines a representative frame110associated with each scene106. For each scene, Si, in set of scenes, Sn, the frame selector108identifies a representative frame using a machine learning model. For example, the frames associated with a scene are extracted and provided to a frame selection model. The frame selection model outputs a quality score associated with each frame. The frame associated with the highest quality score is then identified by the frame selector108as the representative frame. This process is repeated for each scene, resulting in a representative frame110being identified for each scene. These are then passed to preset manager112.

Preset manager112identifies editing settings (also referred to as “edit presets”) for each representative frame, at numeral4. In some embodiments, a machine learning model (e.g., a preset selection model) receives a representative frame and outputs a set of recommended presets based on the content of the input image. For example, in some embodiments, the representative frame is resized to an input resolution associated with the machine learning model. Alternatively, the representative frame is input at its full resolution. In some embodiments, there are multiple machine learning models, each associated with a different input resolution and trained to identify presets for images of a specific resolution. In some embodiments, the preset selection model generates a content embedding for the representative frame and matches the representative frame to images from an image corpus using the content embedding. Each matched image is associated with editing settings and the editing settings are returned as presets per scene114. These presets are provided to preset selector116at numeral5.

At numeral6, preset selector116identifies a preset to be used to edit each scene. In some embodiments, the presets114are presented to a user via a graphical user interface (GUI). The user then selects (e.g., via user input118) the preset to be used for each scene. In some embodiments, preset selector116calculates an aesthetic score for each preset and prunes the presets based on the aesthetic score. The user then selects a preset for each scene from among the pruned presets. This simplifies the selection by the user by removing presets that do not result in improved aesthetics for the scenes.

Once the selected presets120have been identified, they are provided to video editor122at numeral7. The video editor122facilitates the generation, modification, accessing, storing, and/or deletion of digital video content. For example, in some embodiments, the video editor122edits each scene of the input video using the selected presets. In various embodiments, the selected presets include settings for exposure, contrast, white balance, highlights, clarity, etc. which are then applied to each scene using the timestamps identified above. Once the edits have been applied to the scenes by the video editor122, the resulting edited output video124is output at numeral8. For example, the edited output video124is presented to the user via the GUI who then chooses to keep the current changes, apply different presets to one or more of the scenes, etc.

FIG.2illustrates a diagram of a process of scene segmentation in accordance with one or more embodiments. As discussed, when an input video102is received, it is first divided into scenes by scene segmentation manager104. As shown inFIG.2, scene segmentation manager104includes a scene detector200and a scene extractor204. Scene detector200identifies boundaries between scenes based on the content of the frames. As discussed, in some embodiments, histogram differences is used to identify the differences in color histograms between consecutive frames. When this difference is large enough (e.g., greater than a threshold value), the timestamp of the later frame is recorded as marking the start of a new scene. Alternative techniques for identifying scenes may also be used. For example, sum of absolute differences (SAD) is a technique that measures the similarity between image blocks. If the similarity is low (e.g., below a threshold) between consecutive frames, then this is used to identify a start of a new scene. Another technique for measuring the differences between frames is the edge change ratio (ECR). The ECR measures the dissimilarity between frames which can similarly be used to automatically identify when a new scene has started in video data.

As discussed above, the scene detector200implements one of various techniques to identify when a new scene has begun in the input video. The output of the scene detector200is a plurality of scene timestamps202, corresponding to at least the start of each scene in the input video102. In some embodiments, the scene timestamps202include both a start timestamp and an end timestamp. Alternatively, the scene timestamps are indexed by any identifier that is associated with a specific frame.

In some embodiments, a scene extractor204extracts the frames associated with each scene from the input video102using the scene timestamps202. For example, the scene extractor204, in some embodiments, creates new video files corresponding to each scene. These are then output as scenes106. As discussed, each scene is then edited separately using scene specific editing settings and the edited scenes are then recombined to form the edited scene. Alternatively, in some embodiments, the scenes106include the input video and a corresponding editing file which includes the scene timestamps. Subsequently, the timestamps in the editing file are used to apply scene-specific editing settings to particular frames of the input video102corresponding to the identified scenes.

FIG.3illustrates a diagram of a process of determining a representative frame in accordance with one or more embodiments. As discussed, a scene includes a plurality of frames of a digital video which share common features. For example, in some embodiments, the frames of a scene have a similar color composition within a particular error threshold, as discussed above. In the example ofFIG.3, a scene, scene 1300, is provided to frame selector108to identify a representative frame306. As shown inFIG.3, scene 1300includes a plurality of frames depicting a scene at a beach, with a substantial portion of the frames including depictions of the sky, the ocean, and the beach itself. As the frames progress in the scene, more and more of the trees become visible in the frame until the scene ends with roughly half the frame depicting trees with the other half depicting the beach, ocean, and sky.

Each frame is provided to frame selector108to identify a representative frame. As shown inFIG.3, frame selector108includes neural network manager302which includes frame selection model304. In some embodiments, neural network manager302hosts a plurality of neural networks or other machine learning models, such as frame selection model304. The neural network manager302includes an execution environment, libraries, and/or any other data needed to execute the machine learning models. In some embodiments, the neural network manager302is associated with dedicated software and/or hardware resources to execute the machine learning models.

The frame selection model analyzes the frames associated with each scene and identifies the representative frame306. In some embodiments, the frame selection model is a Siamese neural network trained to generate quality scores for frames of video. The difference between quality scores for any two frames indicates the estimated quality difference between those frames. Once the frame selection model has generated a quality score for each frame of the scene300, the frame with the highest score is output as the representative frame306.

FIG.4illustrates a diagram of a process of determining editing settings per scene in accordance with one or more embodiments. Once representative frames400have been obtained for each scene of the input video, they are provided to preset manager112. Preset manager112includes a preset selection model404which identifies a set of editing settings (e.g., a “preset”) for an input frame. This allows for preset selection model404to identify a preset for each representative frame and provide these presets406to preset selector116. In some embodiments, the preset manager112includes neural network manager402. As discussed above, a neural network manager provides a hosting and/or execution environment for neural networks to operate in. In some embodiments, neural network manager402and neural network manager302are the same neural network manager which hosts, and facilitates the execution of, multiple neural networks. Alternatively, in some embodiments, multiple, separate, neural network manager instances are used to host and execute different neural networks.

The preset selection model404is trained to generate a content embedding based on the input image. These content embeddings are high dimensional vectors that represent the content of the images. Using the preset selection model404, an embedding is generated for each representative frame and compared to the embeddings previously generated for the images in the library. The model matches the input image (e.g., representative frame) to one or more images from the library by comparing their respective embeddings. For example, in some embodiments, a distance metric is used to identify the closest images from the library to the input image within the embedding space. Once those images have been identified, presets associated with those images that have been previously determined to improve the quality of the images are then recommended for the input image.

Preset selector116receives the presets406and the scene frames408. As discussed, the scene frames408, in some embodiments, have been extracted from the original input video and provided as a separate file. Alternatively, the scene frames408include the entire input video and an editing file that indicates which frames from the input video are associated with which identified scenes. The presets406can include a large number of presets, not all of which improve the appearance of the scene equally. As such, the preset selector116is tasked with reducing the number of presets before they are presented to the user for selection. Alternatively, the preset selector116chooses the preset for each scene automatically, based on a ranking of the aesthetic improvements provided by each preset to each scene.

The preset selector116includes an aesthetic score manager410which determines an aesthetic score for each scene frame when the presets406are applied to them. In some embodiments, the aesthetic score manager410includes an aesthetic model414. Similar to the preset selection model404and the frame selection model304, a neural network manager412provides a hosting and/or execution environment for the aesthetic model414. Likewise, in some embodiments, neural network manager412, neural network manager402, and neural network manager302are the same neural network manager which hosts, and facilitates the execution of, multiple neural networks. Alternatively, in some embodiments, multiple, separate, neural network manager instances are used to host and execute different neural networks.

The aesthetic model414is a neural network that acts as a recommendation engine and is trained using images from an image repository, such as a public or private image library. In some embodiments, the preset selector116uses the aesthetic model to compute an aesthetic score of each frame after applying a preset. For example, one such model is implemented as a convolutional neural network (CNN) which computes an aesthetics/quality score, along with ten aesthetics attributes (interesting content, object emphasis, lighting quality etc.) for any input image. In some embodiments, the aesthetic model includes a feature encoder which generates a feature vector for the input image. The feature encoder, in some embodiments, includes a separate network or is implemented as one or more layers of the CNN. The feature vector is then provided to one or more classifiers which are trained to evaluate the aesthetics of one or more attributes of the image. The output of each classifier is a numerical value indicating a score for the corresponding aesthetic attribute. In some embodiments, the outputs are combined into a single aesthetics/quality score for the image. Alternatively, in some embodiments, aesthetic models having different architectures are used. For example, in some embodiments, the aesthetic model is implemented as any machine learning model that receives an image as input and determines an aesthetics/quality score for the image. The aesthetic scores for each frame using each preset are computed using the aesthetic model.

The preset selector114then uses the aesthetic scores to compute an effectiveness score. This enables the preset selector to identify the presets that result in the highest aesthetic improvement across the frames of a scene. In some embodiments, the effectiveness score of a preset for a given frame represents the normalized difference in the aesthetics score of the frame computed before and after applying the preset.

where ESiis the effectiveness score of a preset for the ithframe in a scene, ASiAfteris the aesthetic score computed for the ithframe after the preset has been applied, and ASiBeforeis the aesthetic score computed for the ithframe before the preset has been applied. Using the effectiveness scores for all of the frames of a scene using a given preset, an average effectiveness score ESavgis computed as:

If the average effectiveness score for a preset is less than a specified threshold EST, then that preset is discarded the recommended preset list. This process is repeated for each preset until a pruned preset list416is generated. In some embodiments, the pruned preset list416is presented to a user via a user interface. The user can then select a preset for each scene through the user interface by providing one or more user inputs118. Once selected, the selected presets418are then made available to the video editor122, as discussed.

FIG.5illustrates an example of aesthetic scores for frames before and after editing settings have been applied, in accordance with one or more embodiments. As shown inFIG.5, an aesthetic score is computed for the frames of a scene. As discussed, to determine the effectiveness of a given preset, the aesthetics of the frame before any edits have been applied is first determined. As shown at500, a score is determined for each frame. In this instance, the aesthetic scores range from 74 to 77 before applying a preset to the video. As discussed, the same preset is applied to all of the frames of a scene. As such, at502, the frames have had the same preset applied and aesthetic scores have been determined. In this example, the aesthetic scores now range, after the preset has been applied, between 77 and 80. Although only four frames of the scene are shown, this is for simplicity of depiction. Any given scene includes a plurality of frames. As discussed, with these scores determined for all of the frames of the scene, the average effectiveness score is determined for the preset for the scene. Once this is determined for all of the presets recommended for the scene, the list of presets is pruned such that the user is presented with a subset of presets that result in the highest aesthetic improvement.

FIG.6illustrates a diagram of an example process600of determining scene-based edit suggestions for scenes of a video in accordance with one or more embodiments. As shown inFIG.6, an input video is obtained at602. In some embodiments, the input video is provided by a user to the video editing recommendation system. For example, the user selects a locally stored video, a remotely stored video (e.g., in a storage service, networked storage device, etc.), captures and streams a video via a video capture device (e.g., a video camera), or otherwise makes the video available to the video editing recommendation system. At604, the video editing recommendation system then segments the input video into multiple scenes. As discussed, in some embodiments, the scenes are divided based on the content of the frames of the video. For example, consecutive frames with similar color composition are determined to belong to the same scene. The scenes are identified by timestamp (e.g., the timestamps associated with a first and last frame of the scene), or other identifier.

At606, a first scene of the input video is selected. As discussed, in some embodiments the scenes are extracted into separate files. Alternatively, each scene is processed individually using the scene timestamps. At608a representative frame of the scene is identified. As discussed, a first machine learning model is used to identify the frame from the frames of the scene. For example, a quality score is determined for each frame and the frame having the highest quality score is determined to be the representative frame. At612, recommended presets are determined using the representative frame. As discussed, a second machine learning model is used to determine the recommended presets. For example, the second machine learning model identifies similar images from a library of images and provides presets associated with the similar images as recommended presets for the representative frame.

The recommended presets include some presets that produce a more aesthetically pleasing output than others. As a result, at612, the recommended presets are pruned to a more manageable size based on the aesthetic improvements they provide to the scene. For example, a third machine learning model is used to determine an aesthetic score for each frame of the scene. The change in aesthetic scores is determined for each frame and averaged, resulting in an average effective score for the preset for a scene. This is determined for each preset recommended for the scene. The recommended presets are pruned based on the average effectiveness scores and then presented for selection. At614, a preset is selected for the scene. For example, in some embodiments, the presets are presented to the user via a user interface and the user then chooses which preset they prefer. Alternatively, a different service or system selects a preset from the pruned preset list. In some embodiments, at616, the timestamps of the scene and the preset are stored.

At618, it is determined whether there are additional scenes in the input video. For example, a timestamp of the current scene being processed is compared to the end time of the input video. Alternatively, the number of scenes that were identified at604is stored and used to determine whether additional scenes remain to be processed. If there are additional scenes, then at620the next scene is retrieved, and processing returns to608. If there are no additional scenes to be processed, then the scenes are edited using their respective presets and the edited scenes are merged to generate the output video at622.

FIG.7illustrates an example of a scene-based editing data structure700, in accordance with one or more embodiments. As discussed, in some embodiments, scene-based editing recommendations are stored as editing data which is then used to apply the scene-specific edits to each scene. In some embodiments, the editing data is metadata which is represented using various data formats, such as JSON, XML, XMP, etc. As shown inFIG.7, the scene-based editing data structure includes scene information like start timestamp702,708and end timestamp704,710. In some embodiments, additional information, such as an optional scene name, is included. For a given scene, the editing settings706,712applicable to that scene are included. In some embodiments, the scene-based editing data is added to a metadata structure associated with the video. In such instances, a field named “SceneCorrections” is added which includes a list of scenes and their corresponding editing settings706,712.

FIG.8illustrates a schematic diagram of video editing recommendation system (e.g., “video editing recommendation system” described above) in accordance with one or more embodiments. As shown, the video editing recommendation system800may include, but is not limited to, user interface manager802, scene segmentation manager804, frame selector806, preset manager808, preset selector810, video editor812, neural network manager814, and storage manager816. The neural network manager814includes a frame selection model818, a preset selection model820, and an aesthetic model822. The storage manager816includes input video824, editing data structure826, and output video828.

As illustrated inFIG.8, the video editing recommendation system800includes a user interface manager802. For example, the user interface manager802allows users to provide input video824to the video editing recommendation system800. In some embodiments, the user interface manager802provides a user interface through which the user uploads the input video824, selects presets to edit each scene, etc., as discussed above. Alternatively, or additionally, the user interface enables the user to download the video from a local or remote storage location (e.g., by providing an address, such as a URL or other endpoint, associated with a video source, storage service, etc.). In some embodiments, the user interface enables a user to link a video capture device, such as a camera or other hardware, to capture video data and provide it to the video editing recommendation system800.

Additionally, the user interface manager802allows users to request the video editing recommendation system800to provide scene-based edit recommendations for the input video. In some embodiments, the user interface manager802enables the user to view the resulting output video and/or request further edits to the video.

As illustrated inFIG.8, the video editing recommendation system800includes a scene segmentation manager804. As discussed, scene segmentation manager804receives the input video824and divides it into multiple scenes. The scene segmentation manager804implements one of various scene segmentation algorithms to divide the scenes based on their content. For example, in some embodiments, the scene segmentation manager implements a histogram differences technique which identifies a new scene when a frame has a sufficiently different color composition from a previous frame. Once the scenes have been identified, the scenes are passed to frame selector806to identify a representative frame.

As illustrated inFIG.8, the video editing recommendation system800includes a frame selector806. As discussed, frame selector806receives scenes of the input video824from scene segmentation manager804. In some embodiments, the frame selector806receives separate files (e.g., where the individual scenes have been extracted and stored as separate video files). Alternatively, frame selector806receives timestamps associated with at least when each scene starts in the input video (e.g., the start frame of each scene or the start and end frames of each scene). The frame selector806then uses frame selection model818to identify a representative frame for the scene. As discussed, the frame selection model818is a neural network trained to determine a quality score for each frame it is provided. After processing each frame of a scene, and determining their corresponding quality scores, the frame with the highest quality score is determined to be the representative frame. This continues until representative frames have been identified for each scene.

As illustrated inFIG.8, the video editing recommendation system800includes a preset manager808. Preset manager808receives the representative frames from frame selector806. Each representative frame is then input to preset selection model820. As discussed, preset selection model generates a content embedding which is used to identify similar images from an image library to the representative image. Recommended presets (e.g., editing settings associated with the similar images) are then identified and provided to preset selector810.

As illustrated inFIG.8, the video editing recommendation system800includes a preset selector810. Although multiple presets are found for a given representative frame, not all of the presets perform equally, and presenting a potentially large number of preset options that do not improve the appearance of the scene provides a generally negative user experience. As such, the preset selector810is responsible for pruning the recommended presets based on their performance. As discussed, the preset selector uses aesthetic model822to calculate an average effectiveness score for each preset for a given scene. The presets with an average effectiveness score lower than a threshold value are discarded from the recommended presets. The resulting pruned list of recommended presets is then presented to the user for selection of the preset to be used for each scene.

As illustrated inFIG.8, the video editing recommendation system800includes a video editor812. The video editor812facilitates the generation, modification, accessing, storing, and/or deletion of digital video content. As discussed, in some embodiments, the video editor812edits each scene of the input video using the selected presets. As discussed, the selected presets include settings for exposure, contrast, white balance, highlights, clarity, etc. which are then applied to each scene using the timestamps identified above. In some embodiments, the video editor812blends between scenes. As discussed, different scenes have different presets applied to them during editing. This can lead to the appearance of hard cuts between successive scenes. Accordingly, in some embodiments, transition frames between scenes are identified. Such transition frames are identified using techniques such as histogram differences, as discussed above. Alternatively, the transition frames include a number of frames before and after the first frame of the next scene. For example, in one implementation, five to ten frames on either side of the scene are identified as transition frames. In some embodiments, the number of transition frames is a parameter that is set by the user. For the transition frames, blended edits are made to make a smoother transition across scenes. For example, given two scenes with a gradual transition where scene 1 has an exposure value of +1.0 and scene 2 has an exposure value of +2.0, the video editor interpolates between the start (e.g., +1.0) and end (e.g., +2.0) exposure values over the transition frames, so that the scene change in the merged video does not appear abrupt. The edited video is then provided as output video828.

As illustrated inFIG.8, the video editing recommendation system800also includes a neural network manager814. Neural network manager814hosts a plurality of neural networks or other machine learning models, such as frame selection model818, preset selection model820, and aesthetic model822. The neural network manager814includes an execution environment, libraries, and/or any other data needed to execute the machine learning models. In some embodiments, the neural network manager814is associated with dedicated software and/or hardware resources to execute the machine learning models. Although depicted inFIG.8as being hosted by a single neural network manager814, in various embodiments the neural networks are hosted in multiple neural network managers and/or as part of different components. For example, in some embodiments, each model818-822is hosted by their own neural network manager, or other host environment, in which the respective neural networks execute, or the models are spread across multiple neural network managers depending on, e.g., the resource requirements of each model, etc.

As illustrated inFIG.8, the video editing recommendation system800also includes the storage manager816. The storage manager816maintains data for the video editing recommendation system800. The storage manager816maintains data of any type, size, or kind as necessary to perform the functions of the video editing recommendation system800. The storage manager816, as shown inFIG.8, includes the input video824. The input video824includes any digital video file that includes a video with multiple scenes, as discussed in additional detail above. As discussed, the input video824is obtained from a local or remote data store or any location accessible to the video editing recommendation system.

As further illustrated inFIG.8, the storage manager816also includes editing data structure826. As discussed above, editing data structure826includes details for how the input video824is to be edited to generate output video828. For example, the editing data structure includes at least the presets selected by the user to be used for each scene, as well as timestamps defining each scene in the input video824, as discussed above. The storage manager816also includes output video828. Output video828is a copy of the input video824after editing has been performed using the selected per-scene presets. In some embodiments, once edited, the input video824is replaced by the output video828. Alternatively, a copy of the input video824is made for editing. This allows for the user to edit the input video without risking losing the input video824should the edits need to be reverted, new edits applied, etc.

Each of the components802-816of the video editing recommendation system800and their corresponding elements (as shown inFIG.8) may be in communication with one another using any suitable communication technologies. It will be recognized that although components802-816and their corresponding elements are shown to be separate inFIG.8, any of components802-816and their corresponding elements may be combined into fewer components, such as into a single facility or module, divided into more components, or configured into different components as may serve a particular embodiment.

The components802-816and their corresponding elements can comprise software, hardware, or both. For example, the components802-816and their corresponding elements can comprise one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices. When executed by the one or more processors, the computer-executable instructions of the video editing recommendation system800can cause a client device and/or a server device to perform the methods described herein. Alternatively, the components802-816and their corresponding elements can comprise hardware, such as a special purpose processing device to perform a certain function or group of functions. Additionally, the components802-816and their corresponding elements can comprise a combination of computer-executable instructions and hardware.

Furthermore, the components802-816of the video editing recommendation system800may, for example, be implemented as one or more stand-alone applications, as one or more modules of an application, as one or more plug-ins, as one or more library functions or functions that may be called by other applications, and/or as a cloud-computing model. Thus, the components802-816of the video editing recommendation system800may be implemented as a stand-alone application, such as a desktop or mobile application. Furthermore, the components802-816of the video editing recommendation system800may be implemented as one or more web-based applications hosted on a remote server. Alternatively, or additionally, the components of the video editing recommendation system800may be implemented in a suit of mobile device applications or “apps.”

FIG.9illustrates a sequence diagram of a video editing system in accordance with one or more embodiments. As shown inFIG.9, a user provides an input video using user interface manager802at numeral1. In some embodiments, the user selects the input video using user interface manager802to navigate a local or remote file system, access a storage service where the video is stored, etc. At numeral2, the input video is provided to scene segmentation manager804. For example, the user selects a user interface element associated with scene-based edit recommendations and identifies the input video to start the editing process.

At numeral3, the scene segmentation manager804divides the input video into scenes. As discussed, the scene segmentation manager804implements one or more scene identification techniques to identify different scenes in the input video automatically, based on the content of the video. For example, one such technique, histogram differences, identifies the frames of scenes based on their color composition relative to the frames of other scenes. Once the scenes have been identified, the scenes are provided to the frame selector806at numeral4. As discussed, the scenes can be extracted into separate files, or the timestamps associated with the scenes can be identified.

At numeral5, the frame selector806determines a representative frame for each scene. As discussed, the frame selector806uses a frame selection model trained to identify a representative frame from among multiple frames. Once a representative frame of each scene has been determined, the frames are provided to the preset manager808at numeral6. At numeral7, the preset manager808uses a preset recommendation model to identify recommended presets (e.g., editing settings) to be applied to the representative frames. The presets are then provided to the preset selector810at numeral8.

At numeral9, the preset selector prunes the presets. For example, the preset selector uses an aesthetic model to determine an aesthetic score for the frames of a scene before the scene has been edited using a preset and after the scene has been edited. Using the aesthetic scores, an average effectiveness score is calculated for each scene for each preset. Presets having an average effectiveness score below a threshold are pruned. Once the preset list has been pruned, at numeral10the user selects a preset to be used for each scene. The selected presets, scene data, etc. are stored in an editing data structure at numeral11. At numeral12, the video editor obtains the editing data structure and, at numeral13, edits each scene using the scene-specific presets defined by the editing data structure. Once completed, the edited output video is returned at numeral14. For example, the output video can be shown in a user interface, via user interface manager802, to the user for review.

FIGS.1-9, the corresponding text, and the examples, provide a number of different systems and devices that provide scene-based edit recommendations for videos. In addition to the foregoing, embodiments can also be described in terms of flowcharts comprising acts and steps in a method for accomplishing a particular result. For example,FIG.10illustrates a flowchart of an exemplary method in accordance with one or more embodiments. The method described in relation toFIG.10may be performed with fewer or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts.

FIG.10illustrates a flowchart1000of a series of acts in a method of determining scene-based edit suggestions for video data in accordance with one or more embodiments. In one or more embodiments, the method1000is performed in a digital medium environment that includes the video editing recommendation system800. The method1000is intended to be illustrative of one or more methods in accordance with the present disclosure and is not intended to limit potential embodiments. Alternative embodiments can include additional, fewer, or different steps than those articulated inFIG.10.

As illustrated inFIG.10, the method1000includes an act1002of receiving, by a user interface manager, an input video comprising video content. As discussed, the user provides the input video, such as by opening the video from a local or remote storage location, providing access to the video stored in a storage service, or otherwise identifies the video in a location accessible to the video editing recommendation system.

As illustrated inFIG.10, the method1000includes an act1004of dividing, by a scene segmentation manager, the input video into a plurality of scenes based on the video content. As discussed, various techniques may be used to automatically identify different scenes in the input video. In some embodiments, dividing the video content into scenes includes using histogram difference to identify the plurality of scenes, wherein a new scene is identified when the histogram difference of consecutive frames of the video content is higher than a threshold value.

As illustrated inFIG.10, the method1000includes an act1006of identifying, by a frame selector, a representative frame for each scene. In some embodiments, a first machine learning model identifies, for each scene, the representative frame. For example, in some embodiments, identifying the representative frame includes providing a plurality of frames associated with a scene to a frame selection model, wherein the frame selection model determines a quality score for each frame of the plurality of frames, and selecting a frame having a highest quality score as the representative frame for the scene.

As illustrated inFIG.10, the method1000includes an act1008of determining, by a preset manager, a plurality of editing settings for each representative frame. The presets for each scene are determined based on the representative frames. In some embodiments, determining the editing settings includes providing the representative frame for each scene to a preset selection model, wherein the preset selection model determines a content embedding for each representative frame and identifies similar images from an image library using the content embedding, and identifying editing settings associated with each similar image to determine the plurality of editing settings.

As illustrated inFIG.10, the method1000includes an act1010of determining, by a preset selector, editing settings for each scene based on an effectiveness score. As discussed, a large number of presets are identified for each scene using the representative frame. Some of these presets perform better at improving the aesthetics of the scene than others. Accordingly, before they are presented to the user, the presets are first pruned based on which perform best. In some embodiments, determining the editing settings includes determining, by the preset selector, a first aesthetic score for each frame of a first scene using an aesthetic model, wherein the aesthetic model receives each frame and outputs the first aesthetic score associated with each frame. In some embodiments, determining the editing settings includes applying a first editing setting to a first scene to create an edited first scene, determining a second aesthetic score for each frame of the edited first scene using the aesthetic model, and calculating an average effectiveness score for the first scene based on a change in aesthetic scores between the first aesthetic score and the second aesthetic score.

As illustrated inFIG.10, the method1000includes an act1012of generating, by a video editor, an output video using the input video and the editing settings for each scene. In some embodiments, the selected presets and the scene information (e.g., timestamps, etc.) are added to an editing data structure, such as a metadata file, which is used by the video editor to apply the selected presets to their associated scenes. In some embodiments, generating the output video includes receiving a selection of an editing setting for each scene of the video content, and applying the selected editing setting to each scene to generate the output video. Additionally, in some embodiments, the edits are blended between scenes, to reduce any visible artifacts associated with different scenes having different editing settings. In such embodiments, generating the output video further includes identifying a plurality of transition frames between a first scene and a second scene in the video content, determining blended editing settings based on first editing settings associated with the first scene and second editing settings associated with the second editing scene, and applying the blended editing settings to the transition frames.

FIG.11illustrates a schematic diagram of an exemplary environment1100in which the video editing recommendation system800can operate in accordance with one or more embodiments. In one or more embodiments, the environment1100includes a service provider1102which may include one or more servers1104connected to a plurality of client devices1106A-1106N via one or more networks1108. The client devices1106A-1106N, the one or more networks1108, the service provider1102, and the one or more servers1104may communicate with each other or other components using any communication platforms and technologies suitable for transporting data and/or communication signals, including any known communication technologies, devices, media, and protocols supportive of remote data communications, examples of which will be described in more detail below with respect toFIG.12.

AlthoughFIG.11illustrates a particular arrangement of the client devices1106A-1106N, the one or more networks1108, the service provider1102, and the one or more servers1104, various additional arrangements are possible. For example, the client devices1106A-1106N may directly communicate with the one or more servers1104, bypassing the network1108. Or alternatively, the client devices1106A-1106N may directly communicate with each other. The service provider1102may be a public cloud service provider which owns and operates their own infrastructure in one or more data centers and provides this infrastructure to customers and end users on demand to host applications on the one or more servers1104. The servers may include one or more hardware servers (e.g., hosts), each with its own computing resources (e.g., processors, memory, disk space, networking bandwidth, etc.) which may be securely divided between multiple customers, each of which may host their own applications on the one or more servers1104. In some embodiments, the service provider may be a private cloud provider which maintains cloud infrastructure for a single organization. The one or more servers1104may similarly include one or more hardware servers, each with its own computing resources, which are divided among applications hosted by the one or more servers for use by members of the organization or their customers.

Similarly, although the environment1100ofFIG.11is depicted as having various components, the environment1100may have additional or alternative components. For example, the environment1100can be implemented on a single computing device with the video editing recommendation system800. In particular, the video editing recommendation system800may be implemented in whole or in part on the client device1102A.

As illustrated inFIG.11, the environment1100may include client devices1106A-1106N. The client devices1106A-1106N may comprise any computing device. For example, client devices1106A-1106N may comprise one or more personal computers, laptop computers, mobile devices, mobile phones, tablets, special purpose computers, TVs, or other computing devices, including computing devices described below with regard toFIG.12. Although three client devices are shown inFIG.11, it will be appreciated that client devices1106A-1106N may comprise any number of client devices (greater or smaller than shown).

Moreover, as illustrated inFIG.11, the client devices1106A-1106N and the one or more servers1104may communicate via one or more networks1108. The one or more networks1108may represent a single network or a collection of networks (such as the Internet, a corporate intranet, a virtual private network (VPN), a local area network (LAN), a wireless local network (WLAN), a cellular network, a wide area network (WAN), a metropolitan area network (MAN), or a combination of two or more such networks. Thus, the one or more networks1108may be any suitable network over which the client devices1106A-1106N may access service provider1102and server1104, or vice versa. The one or more networks1108will be discussed in more detail below with regard toFIG.12.

In addition, the environment1100may also include one or more servers1104. The one or more servers1104may generate, store, receive, and transmit any type of data, including input video824, editing data structure826, and output video828, or other information. For example, a server1104may receive data from a client device, such as the client device1106A, and send the data to another client device, such as the client device1102B and/or1102N. The server1104can also transmit electronic messages between one or more users of the environment1100. In one example embodiment, the server1104is a data server. The server1104can also comprise a communication server or a web-hosting server. Additional details regarding the server1104will be discussed below with respect toFIG.12.

As mentioned, in one or more embodiments, the one or more servers1104can include or implement at least a portion of the video editing recommendation system800. In particular, the video editing recommendation system800can comprise an application running on the one or more servers1104or a portion of the video editing recommendation system800can be downloaded from the one or more servers1104. For example, the video editing recommendation system800can include a web hosting application that allows the client devices1106A-1106N to interact with content hosted at the one or more servers1104. To illustrate, in one or more embodiments of the environment1100, one or more client devices1106A-1106N can access a webpage supported by the one or more servers1104. In particular, the client device1106A can run a web application (e.g., a web browser) to allow a user to access, view, and/or interact with a webpage or website hosted at the one or more servers1104.

Upon the client device1106A accessing a webpage or other web application hosted at the one or more servers1104, in one or more embodiments, the one or more servers1104can provide access to one or more digital videos (e.g., the input video data824) stored at the one or more servers1104. Moreover, the client device1106A can receive a request (i.e., via user input) to determine scene-based editing recommendations and provide the request to the one or more servers1104. Upon receiving the request, the one or more servers1104can automatically perform the methods and processes described above to identify editing recommendations for the scenes of the input video and edit the video accordingly to generate an output video. The one or more servers1104can provide the output video to the client device1106A for display to the user.

As just described, the video editing recommendation system800may be implemented in whole, or in part, by the individual elements1102-1108of the environment1100. It will be appreciated that although certain components of the video editing recommendation system800are described in the previous examples with regard to particular elements of the environment1100, various alternative implementations are possible. For instance, in one or more embodiments, the video editing recommendation system800is implemented on any of the client devices1106A-N. Similarly, in one or more embodiments, the video editing recommendation system800may be implemented on the one or more servers1104. Moreover, different components and functions of the video editing recommendation system800may be implemented separately among client devices1106A-1106N, the one or more servers1104, and the network1108.

FIG.12illustrates, in block diagram form, an exemplary computing device1200that may be configured to perform one or more of the processes described above. One will appreciate that one or more computing devices such as the computing device1200may implement the video editing recommendation system. As shown byFIG.12, the computing device can comprise a processor1202, memory1204, one or more communication interfaces1206, a storage device1208, and one or more I/O devices/interfaces1210. In certain embodiments, the computing device1200can include fewer or more components than those shown inFIG.12. Components of computing device1200shown inFIG.12will now be described in additional detail.

In particular embodiments, processor(s)1202includes hardware for executing instructions, such as those making up a computer program. As an example, and not by way of limitation, to execute instructions, processor(s)1202may retrieve (or fetch) the instructions from an internal register, an internal cache, memory1204, or a storage device1208and decode and execute them. In various embodiments, the processor(s)1202may include one or more central processing units (CPUs), graphics processing units (GPUs), field programmable gate arrays (FPGAs), systems on chip (SoC), or other processor(s) or combinations of processors.

The computing device1200includes memory1204, which is coupled to the processor(s)1202. The memory1204may be used for storing data, metadata, and programs for execution by the processor(s). The memory1204may include one or more of volatile and non-volatile memories, such as Random Access Memory (“RAM”), Read Only Memory (“ROM”), a solid state disk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of data storage. The memory1204may be internal or distributed memory.

The computing device1200can further include one or more communication interfaces1206. A communication interface1206can include hardware, software, or both. The communication interface1206can provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device and one or more other computing devices1200or one or more networks. As an example and not by way of limitation, communication interface1206may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. The computing device1200can further include a bus1212. The bus1212can comprise hardware, software, or both that couples components of computing device1200to each other.

The computing device1200includes a storage device1208includes storage for storing data or instructions. As an example, and not by way of limitation, storage device1208can comprise a non-transitory storage medium described above. The storage device1208may include a hard disk drive (HDD), flash memory, a Universal Serial Bus (USB) drive or a combination these or other storage devices. The computing device1200also includes one or more input or output (“I/O”) devices/interfaces1210, which are provided to allow a user to provide input to (such as user strokes), receive output from, and otherwise transfer data to and from the computing device1200. These I/O devices/interfaces1210may include a mouse, keypad or a keyboard, a touch screen, camera, optical scanner, network interface, modem, other known I/O devices or a combination of such I/O devices/interfaces1210. The touch screen may be activated with a stylus or a finger.

In the foregoing specification, embodiments have been described with reference to specific exemplary embodiments thereof. Various embodiments are described with reference to details discussed herein, and the accompanying drawings illustrate the various embodiments. The description above and drawings are illustrative of one or more embodiments and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of various embodiments.