A high-efficiency processing and editing technology system for digital intermediates

Abstract

The present invention discloses a digital intermediate (DI) efficient processing and editing technology system, which relates to the field of intelligent DI technology. The system comprises: a DI import and analysis module for importing an original DI and analyzing its content; an efficient processing and editing module for editing, synthesizing, and modifying the original DI to obtain an edited DI, wherein the efficient processing and editing module integrates a high-performance processing engine; an image enhancement module for performing image enhancement on the edited DI to obtain an enhanced DI; a data security management module for performing data encryption processing on the enhanced DI using data encryption technology to obtain a processed DI; and a multi-platform compatibility and export module for exporting the processed DI. In this way, fast, accurate, and high-quality processing and enhancement of DI can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent digital intermediate films, and more particularly to a digital intermediate film efficient processing and editing technology system. Background Art

[0002] Digital Intermediate (DI) plays a very important role in film production. DI refers to the digitization of the original film material and the presentation of the film material in the process of post-processing and editing.

[0003] Traditional processing and editing tools can present challenges when working with digital intermediates. For example, their functionality can be relatively limited, failing to meet the complex demands of film production, such as precise editing and intelligent image enhancement. Furthermore, traditional tools can lack intuitive editing interfaces and user-friendly operation, making the editing process complex and difficult.

[0004] Therefore, an optimized digital intermediate efficient processing and editing technology system is expected. Summary of the Invention

[0005] To address the above-mentioned technical problems, the present invention is proposed. An embodiment of the present invention provides a system for efficiently processing and editing digital intermediates, comprising: a digital intermediate import and parsing module for importing an original digital intermediate and parsing its content; an efficient processing and editing module for editing, synthesizing, and modifying the original digital intermediate to obtain an edited digital intermediate, wherein the efficient processing and editing module integrates a high-performance processing engine; an image enhancement module for performing image enhancement on the edited digital intermediate to obtain an enhanced digital intermediate; a data security management module for performing data encryption processing on the enhanced digital intermediate using data encryption technology to obtain a processed digital intermediate; and a multi-platform compatibility and export module for exporting the processed digital intermediate. This system enables rapid, accurate, and high-quality processing and enhancement of digital intermediates.

[0006] In a first aspect, a system for efficiently processing and editing digital intermediates is provided, comprising:

[0007] A digital intermediate import and analysis module, used for importing an original digital intermediate and analyzing the content of the original digital intermediate;

[0008] an efficient processing and editing module, for editing, synthesizing and modifying the original digital intermediate to obtain an edited digital intermediate, wherein the efficient processing and editing module integrates a high-performance processing engine;

[0009] An image enhancement module, configured to perform image enhancement on the edited digital intermediate to obtain an enhanced digital intermediate, comprising: a digital intermediate acquisition unit, configured to acquire the edited digital intermediate;

[0010] a to-be-processed image frame extraction unit, configured to extract the to-be-processed image frame from the edited digital intermediate;

[0011] A multi-level image feature extraction unit, configured to extract multi-level image features of the image frame to be processed to obtain a color-level feature map of the image frame to be processed and a texture-level feature map of the image frame to be processed;

[0012] a fusion unit, configured to fuse the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed to obtain a multi-scale feature map of the image frame to be processed;

[0013] and an enhanced image frame generating unit, configured to generate an enhanced image frame based on the multi-scale feature map of the image frame to be processed;

[0014] a data security management module, configured to perform data encryption processing on the enhanced digital intermediate using a data encryption technology to obtain a processed digital intermediate;

[0015] and a multi-platform compatibility and export module for exporting the processed digital intermediate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 FIG. 4 is a block diagram of a system for efficiently processing and editing digital intermediates according to an embodiment of the present invention.

[0018] Figure 2 2 is a schematic structural diagram of a digital intermediate efficient processing and editing technology system according to an embodiment of the present invention.

[0019] Figure 3 Flowchart of a technical method for efficient processing and editing of digital intermediates according to an embodiment of the present invention.

[0020] Figure 4 Schematic diagram of a technical method architecture for efficient processing and editing of digital intermediates according to an embodiment of the present invention.

[0021] Figure 5This is a diagram of an application scenario of a digital intermediate efficient processing and editing technology system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following describes the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] Unless otherwise specified, all technical and scientific terms used in the embodiments of the present invention have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the scope of the present invention.

[0024] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified and limited, the term "connection" should be understood in a broad sense. For example, it can be an electrical connection, or it can be a connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meaning of the above terms can be understood according to the specific circumstances.

[0025] It should be noted that the terms "first, second, and third" used in the embodiments of the present invention are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that the terms "first, second, and third" may interchangeably represent a specific order or precedence, where permitted. It should be understood that the terms "first, second, and third" may interchangeably represent objects, where appropriate, such that the embodiments of the present invention described herein may be implemented in an order other than that illustrated or described herein.

[0026] After introducing the basic principles of the present invention, various non-limiting embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Digital Intermediate (DI) is a technology and process widely used in film production, which involves converting the original materials of a film (including film and digital photography) into digital format and adjusting and processing them during post-processing and editing to achieve the intentions of the director and producer.

[0028] The digital intermediate (DI) process typically involves several steps: Photography and Acquisition: Films are typically shot using film or digital cameras. During this stage, the raw footage is captured onto film or a digital sensor. Digital Scanning: If film is used, it is scanned into a digital format. This process converts each frame of the film into a digital image, creating a high-resolution digital image sequence. Image Processing: Once the raw footage is digitized, adjustments and processing can be performed on the image. This includes color correction, contrast adjustment, color grading, and the addition of special effects. These adjustments can be performed using specialized DI software. Special Effects and Restoration: DIs also support special effects and restoration. Special effects can be added to a film during the DI process, while restoration can be used to correct defects or damage in film or digital footage. Post-Editing: During the DI process, film editing can also be performed, including editing, adding music, and sound design. Output and Delivery: After the DI is completed, the film can be output to various formats, including digital projection, Blu-ray, and DVD, depending on the film's intended use.

[0029] Digital intermediates (DIs) provide high-quality image processing and adjustments to enhance the visual quality of films. They allow for extensive adjustments and revisions during post-production to meet the requirements of directors and producers. DIs can improve post-production efficiency, reducing the steps and time required for traditional film production. DIs also preserve films in a digital format for easy storage and transfer, as well as for backup and archiving.

[0030] Traditional processing and editing tools do have some problems when it comes to digital intermediates. Traditional tools may have relatively limited functionality and be unable to meet the complex demands of film production. For example, some tools may lack advanced image processing capabilities, preventing precise editing and intelligent image enhancement. Traditional tools may lack an intuitive editing interface, making the editing process complex and difficult, which may require users to possess professional skills and experience to operate the tools proficiently. Traditional tools may also be relatively complex to operate, requiring users to perform multiple steps and set parameters, which can make the editing process lengthy and cumbersome. Due to the limitations and complexity of traditional tools, the processing and editing process may require longer time and more human resources, which may have an adverse impact on production schedules and costs. Traditional tools may also have compatibility issues across different platforms and systems, which can make file format conversion and processing difficult, adding hassles and risks to the workflow.

[0031] To address these issues, modern digital intermediate tools and software continue to evolve and improve, offering richer feature sets, including advanced image processing, special effects, and restoration capabilities, to meet the demands of complex productions. Furthermore, modern tools focus on providing intuitive user interfaces and user-friendly operations to streamline the editing process and improve efficiency. These tools also support seamless integration across various file formats and platforms, providing a more convenient and reliable workflow.

[0032] In one embodiment of the present invention, Figure 1 FIG. 1 is a block diagram of a digital intermediate efficient processing and editing system according to an embodiment of the present invention. Figure 1 As shown, the digital intermediate efficient processing and editing technology system 100 according to an embodiment of the present invention includes: a digital intermediate importing and parsing module 110, used to import the original digital intermediate and parse the content of the original digital intermediate; an efficient processing and editing module 120, used to edit, synthesize and modify the original digital intermediate to obtain an edited digital intermediate, wherein the efficient processing and editing module integrates a high-performance processing engine; an image enhancement module 130, used to perform image enhancement on the edited digital intermediate to obtain an enhanced digital intermediate; a data security management module 140, used to perform data encryption processing on the enhanced digital intermediate using data encryption technology to obtain a processed digital intermediate; and a multi-platform compatibility and export module 150, used to export the processed digital intermediate.

[0033] The digital intermediate import and parsing module 110 correctly imports the original digital intermediate and parses its content, handling various file formats and encoding schemes. This efficient import and parsing ensures the correct loading and processing of the original digital intermediate, providing an accurate data foundation for subsequent processing and editing steps.

[0034] The high-performance processing and editing module 120 includes a high-performance processing engine to ensure efficient processing of large amounts of data and complex operations when editing, compositing, and retouching the original digital intermediate. This integrated high-performance processing engine improves processing speed and efficiency, reducing processing time, thereby accelerating production schedules and enhancing productivity.

[0035] Image enhancement module 130 provides image enhancement functions, including color correction, contrast adjustment, and color grading, to improve the visual quality of the edited digital intermediate. This enhancement improves the visual quality of the edited digital intermediate, making it more appealing and high-quality, while also meeting the expectations of directors and producers.

[0036] The data security management module 140 employs data encryption technology to securely process the enhanced digital intermediate to protect its confidentiality and integrity. This data encryption protects the processed digital intermediate during transmission and storage, preventing unauthorized access and tampering, and ensuring data security.

[0037] The multi-platform compatibility and export module 150 supports multiple platforms and file formats to enable export and delivery of digital intermediates. This multi-platform compatibility and export module ensures that the processed digital intermediate can be played, transmitted, and subsequently processed on different platforms and systems, thereby improving the usability and flexibility of the digital intermediate.

[0038] The specific content of the digital intermediate efficient processing and editing technology system can be as follows: Figure 2 As shown, the digital intermediate import and parsing module includes: metadata processing sub-module, color space processing sub-module and material processing sub-module; the efficient processing and editing block includes: rough cutting sub-module, fine cutting sub-module and sample output sub-module; the special effects processing and enhancement module includes: special effects application sub-module, color correction sub-module and image enhancement sub-module; the data security and version management module includes: master production sub-module and key management sub-module; the multi-platform compatibility and export module includes: DCP production sub-module, IMF production sub-module, network version production sub-module and other version production sub-modules.

[0039] Traditional methods for enhancing digital intermediates (DIs) are typically based on manual rules and experience, resulting in results that often rely on subjective judgment. This means different people may achieve different enhancement results, lacking consistency and objectivity. Furthermore, this approach requires extensive manual intervention and adjustments, consuming significant time and labor costs. This can become impractical for large-scale image processing.

[0040] In response to the above technical problems, the technical concept of the present invention is to extract features from different angles on the image frames to be processed of the edited digital intermediate, and to complement and blend the feature distributions with each other, so as to intelligently generate enhanced image frames, thereby achieving fast, accurate and high-quality processing and enhancement of the digital intermediate.

[0041] In one embodiment of the present invention, the image enhancement module includes: a digital intermediate acquisition unit for acquiring the edited digital intermediate; a to-be-processed image frame extraction unit for extracting the to-be-processed image frame from the edited digital intermediate; a multi-level image feature extraction unit for extracting multi-level image features of the to-be-processed image frame to obtain a color-level feature map of the to-be-processed image frame and a texture-level feature map of the to-be-processed image frame; a fusion unit for fusing the color-level feature map of the to-be-processed image frame and the texture-level feature map of the to-be-processed image frame to obtain a multi-scale feature map of the to-be-processed image frame; and an enhanced image frame generation unit for generating an enhanced image frame based on the multi-scale feature map of the to-be-processed image frame.

[0042] The digital intermediate acquisition unit is used to obtain the edited digital intermediate to ensure that subsequent processing steps can operate based on correct data. The image frame extraction unit to be processed extracts the image frame to be processed from the edited digital intermediate to provide input data for subsequent image processing steps. The multi-level image feature extraction unit: extracts multi-level image features of the image frame to be processed, including color-level features and texture-level features. These feature extractions can capture the details and characteristics of the image and provide rich information for subsequent image enhancement steps. The fusion unit fuses the color-level features and texture-level features of the image frame to be processed to generate a multi-scale feature map of the image frame to be processed. This fusion can integrate feature information at different levels to provide a more comprehensive and accurate feature representation. The enhanced image frame generation unit generates an enhanced image frame based on the multi-scale feature map of the image frame to be processed. By utilizing multi-level feature information, it can achieve fine enhancement of the image, improve the image quality, contrast, clarity, etc., and make the image more attractive and high-quality.

[0043] These units jointly promote the image enhancement and quality improvement of digital intermediate films. By extracting and fusing multi-level image features and generating enhanced image frames based on feature maps, fine processing and optimization of images can be achieved, improving visual effects and viewing experience.

[0044] Based on this, the technical solution of the present invention first obtains the edited digital intermediate and then extracts the image frames to be processed from the edited digital intermediate. In an embodiment of the present invention, a video processing library or framework (such as OpenCV) is used to read the video frame by frame, and each frame is used as the image frame to be processed for subsequent processing.

[0045] It should be understood that color is an important visual feature in an image and has a significant impact on image perception and understanding. In addition, texture information depicts the local structure and detailed features in an image and can describe the texture type, texture direction, texture density, etc. in the image. Therefore, in the technical solution of the present invention, the image frame to be processed is passed through a color feature extractor based on a first convolutional neural network model to obtain a color-level feature map of the image frame to be processed; at the same time, the color-level feature map of the image frame to be processed is passed through a texture feature extractor based on a second convolutional neural network model to obtain a texture-level feature map of the image frame to be processed. In other words, the color feature extractor is used to capture features such as color distribution and saturation changes in the image. The texture feature extractor is used to capture texture changes and texture details of the image, such as surface texture and texture edges. In this way, the features of the image frame to be processed are described with different emphases to guide the model to learn and understand the information conveyed by the image frame to be processed.

[0046] In a specific embodiment of the present invention, the multi-level image feature extraction unit includes: a color feature extraction subunit, used to perform feature extraction on the image frame to be processed using a first deep learning network model to obtain a color-level feature map of the image frame to be processed; and a texture feature extraction subunit, used to perform feature extraction on the image frame to be processed using a second deep learning network model to obtain a texture-level feature map of the image frame to be processed.

[0047] Among them, the first deep learning network model is a color feature extractor based on a first convolutional neural network model, and the second deep learning network model is a texture feature extractor based on a second convolutional neural network model.

[0048] Furthermore, the color feature extractor of the first convolutional neural network model includes: a first input layer, a first convolutional layer, a first pooling layer, a first activation layer and a first output layer; the texture feature extractor based on the second convolutional neural network model includes: a second input layer, a second convolutional layer, a second pooling layer, a second activation layer and a second output layer.

[0049] In a specific embodiment of the present invention, the color feature extraction subunit is used to: pass the image frame to be processed through the color feature extractor based on the first convolutional neural network model to obtain a color-level feature map of the image frame to be processed.

[0050] In a specific embodiment of the present invention, the texture feature extraction subunit is used to: pass the color-level feature map of the image frame to be processed through the texture feature extractor based on the second convolutional neural network model to obtain the texture-level feature map of the image frame to be processed.

[0051] The color feature extraction subunit uses the first deep learning network model to extract features from the image frame to be processed, generating a color-level feature map. This map reflects information such as color distribution, color saturation, and brightness within the image. This map can be used for image processing operations such as color correction, contrast adjustment, and color grading, thereby improving the visual quality of the image.

[0052] The texture feature extraction subunit uses the second deep learning network model to extract features from the image frame being processed, generating a texture-level feature map for the frame. This map reflects information such as texture detail, texture orientation, and texture density. This information can be used for image sharpening, detail enhancement, and texture enhancement, thereby enhancing the image's detail and texture.

[0053] The feature extraction of the image frame to be processed is achieved through the deep learning network model, thereby providing rich color-level features and texture-level features to be utilized by subsequent fusion units, further improving the image enhancement effect and making the processed image more vivid, clear and with good texture.

[0054] Considering that the combined utilization of color and texture features can more comprehensively describe the features of the image frame to be processed, better preserve the essential characteristics of the image, and provide important support for subsequent image enhancement, the technical solution of the present invention further utilizes an information compensation transfer module to fuse the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed to obtain a multi-scale feature map of the image frame to be processed. Specifically, color and texture features provide information at different levels and angles within the image. Color features primarily focus on the color distribution and hue of the image, while texture features focus on the local structure and details of the image. By fusing these two features, the overall color and detailed texture of the image can be comprehensively considered, resulting in a richer and more comprehensive feature representation. The use of the information compensation transfer module allows for information compensation and adjustment during the fusion process to address the deficiencies of each. For example, if the color-level feature map lacks detailed information in certain areas, the texture-level feature map can provide richer detailed information. Through the adjustment of the information compensation transfer module, this detailed information can be rationally incorporated into the final feature representation.

[0055] In a specific embodiment of the present invention, the fusion unit is used to: use an information compensation transfer module to fuse the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed to obtain the multi-scale feature map of the image frame to be processed.

[0056] Specifically, the fusion unit includes: an upsampling and convolution processing subunit, which is used to upsample and convolve the texture-level feature map of the image frame to be processed to obtain a reconstructed texture-level feature map of the image frame to be processed; a position difference calculation subunit, which is used to calculate the position difference between the reconstructed texture-level feature map of the image frame to be processed and the color-level feature map of the image frame to be processed to obtain a difference feature map; a nonlinear activation processing subunit, which is used to perform nonlinear activation processing based on the Sigmoid function on the difference feature map to obtain a mask feature map; a feature map calculation subunit, which is used to perform point multiplication on the color-level feature map of the image frame to be processed and the mask feature map to obtain a fused feature map; and a pooling operation subunit, which is used to perform an attention-based PMA pooling operation on the fused feature map to obtain a multi-scale feature map of the image frame to be processed.

[0057] Here, considering that it is precisely because color features and texture features provide information at different levels and angles in the image, the color features of the image frame to be processed lack the local structure and detail information of the image, while the texture features of the image frame to be processed lack the color information of the image. In particular, the information compensation and transmission module constructs the difference information of the color features and texture features in the image frame to be processed by calculating the positional difference between the reconstructed texture-level feature map of the image frame to be processed and the color-level feature map of the image frame to be processed, and appends this difference information to the color-level feature map of the image frame to be processed, thereby blending and complementing the features of the two, so that the fused multi-scale feature map of the image frame to be processed has richer feature expression.

[0058] In one embodiment of the present invention, the enhanced image frame generation unit includes: an optimization subunit, configured to optimize the multi-scale feature map of the image frame to be processed to obtain an optimized multi-scale feature map of the image frame to be processed; and an image enhancement subunit, configured to pass the optimized multi-scale feature map of the image frame to be processed through an image enhancer based on a generative adversarial network to obtain the enhanced image frame.

[0059] Here, the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed respectively express the color image semantic features and texture image semantic features of the image frame to be processed, and both have specific spatial meanings based on image semantic feature extraction of convolutional neural networks in the spatial distribution dimension within the feature matrix and the channel distribution dimension between feature matrices. In this way, when using the information compensation transfer module to fuse the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed, considering the inter-layer residual-based eigenvalue granularity calculation properties of the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed, due to the difference in spatial distribution correspondence between the color image semantic features and the texture image semantic features, the eigenvalues ​​of the obtained multi-scale feature map of the image frame to be processed will have sparse probability density representation in the probability density domain, thereby affecting the image quality of the enhanced image frame obtained by the image enhancer based on the adversarial generative network.

[0060] Based on this, the present application provides a multi-scale feature map of the image frame to be processed. Optimization is performed, which is expressed as: optimizing the multi-scale feature map of the image frame to be processed using the following optimization formula to obtain an optimized multi-scale feature map of the image frame to be processed; wherein the optimization formula is:

[0061]

[0062]

[0063] in, Represents the multi-scale feature map of the image frame to be processed The position-wise square graph of It is a parameter-trainable intermediate weight map, for example, based on the inter-layer residual calculation property between the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed, the eigenvalue of each feature matrix is ​​initially set to be the multi-scale feature map of the image frame to be processed The eigenvalue mean of the corresponding characteristic matrix of , in addition, is the unit graph with all eigenvalues ​​1, represents the transition graph, represents a position-wise square graph of the transition graph, represents the positional addition of feature maps, represents the position-wise multiplication of the feature map, Represents the optimized multi-scale feature map of the image frame to be processed.

[0064] Here, in order to optimize the multi-scale feature map of the image frame to be processed The sparse probability density of the image is evenly distributed and consistent in the overall probability space, and the multi-scale feature map of the image frame to be processed is strengthened by the tail distribution mechanism of the standard Cauchy distribution. The distance distribution in the high-dimensional feature space is optimized based on the spatial angle tilt to achieve the multi-scale feature map of the image frame to be processed. The feature distribution space resonance of each local feature distribution is weakly correlated with the distance of each local feature distribution, thereby improving the multi-scale feature map of the image frame to be processed The overall uniformity and consistency of the probability density distribution relative to the regression probability convergence improve the image quality of the enhanced image frames obtained by the image enhancer based on the adversarial generative network.

[0065] The optimized multi-scale feature map of the image frame to be processed is then passed through an image enhancer based on a generative adversarial network (GAN) to produce an enhanced image frame. Generative adversarial networks (GANs) consist of a generator and a discriminator. The generator receives the multi-scale feature map of the image frame to be processed as input and maps it to produce the enhanced image frame. The discriminator is responsible for determining whether the enhanced image frame is from a real image or one generated by the generator. Through adversarial training between the generator and the discriminator, the generator gradually learns how to produce more realistic enhanced images.

[0066] It should be understood that the present invention takes image enhancement of one frame as an example. In actual applications, each image frame requiring image enhancement should be input for multiple processing.

[0067] The digital intermediate efficient processing and editing technology system of the present invention has the following advantages:

[0068] Efficiency: It integrates a high-performance processing engine and fast editing tools to provide fast and efficient editing and processing functions.

[0069] Accuracy: During the processing and editing process, the original information from the camera to the projection terminal is accurately retained, and multi-track editing and real-time preview are supported to ensure the accuracy and high quality of the editing effect.

[0070] Security: Data encryption technology is used to ensure the security of digital intermediate data and provide version management and control.

[0071] Compatibility: Supports multiple format output and multi-platform compatibility to meet production needs in different environments.

[0072] In summary, the efficient digital intermediate processing and editing technology system 100 based on the embodiment of the present invention is explained, which extracts features from different angles on the image frames to be processed of the edited digital intermediate, and complements and blends the feature distributions with each other to intelligently generate enhanced image frames, thereby achieving fast, accurate and high-quality processing and enhancement of the digital intermediate.

[0073] As described above, the efficient digital intermediate processing and editing technology system 100 according to an embodiment of the present invention can be implemented in various terminal devices, such as a server for efficient digital intermediate processing and editing technology. In one example, the efficient digital intermediate processing and editing technology system 100 according to an embodiment of the present invention can be integrated into a terminal device as a software module and / or a hardware module. For example, the efficient digital intermediate processing and editing technology system 100 can be a software module in the terminal device's operating system, or it can be an application developed specifically for the terminal device. Of course, the efficient digital intermediate processing and editing technology system 100 can also be one of the terminal device's many hardware modules.

[0074] Alternatively, in another example, the digital intermediate film efficient processing and editing technology system 100 and the terminal device can also be separate devices, and the digital intermediate film efficient processing and editing technology system 100 can be connected to the terminal device through a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0075] In one embodiment of the present invention, Figure 3 FIG. 1 is a flow chart of a method for efficiently processing and editing digital intermediate films according to an embodiment of the present invention. Figure 3 As shown, the technical method for efficient processing and editing of digital intermediates includes: 210, importing an original digital intermediate and parsing the content of the original digital intermediate; 220, editing, synthesizing and modifying the original digital intermediate to obtain an edited digital intermediate, wherein the efficient processing and editing module integrates a high-performance processing engine; 230, performing image enhancement on the edited digital intermediate to obtain an enhanced digital intermediate; 240, performing data encryption processing on the enhanced digital intermediate using data encryption technology to obtain a processed digital intermediate; and, 250, exporting the processed digital intermediate.

[0076] Figure 4 Schematic diagram of the structure of the digital intermediate efficient processing and editing technology method according to an embodiment of the present invention. Figure 4As shown, image enhancement is performed on the edited digital intermediate to obtain an enhanced digital intermediate, including: first, obtaining the edited digital intermediate; then, extracting an image frame to be processed from the edited digital intermediate; next, extracting multi-level image features of the image frame to be processed to obtain a color-level feature map of the image frame to be processed and a texture-level feature map of the image frame to be processed; then, fusing the color-level feature map of the image frame to be processed and the texture-level feature map of the image frame to be processed to obtain a multi-scale feature map of the image frame to be processed; and finally, generating an enhanced image frame based on the multi-scale feature map of the image frame to be processed.

[0077] Those skilled in the art will appreciate that the specific operations of each step in the above-mentioned digital intermediate efficient processing and editing technology method have been described in detail above. Figures 1 to 2 The description of the digital intermediate efficient processing and editing technology system has been introduced in detail, and therefore, its repeated description will be omitted.

[0078] Figure 5 FIG is an application scenario diagram of the digital intermediate film efficient processing and editing technology system according to an embodiment of the present invention. Figure 5 As shown, in this application scenario, first, the edited digital intermediate (for example, Figure 5 Then, the obtained edited digital intermediate is input to a server (for example, Figure 5 In S) as shown in , the server is capable of processing the edited digital intermediate based on a digital intermediate efficient processing and editing technology algorithm to generate an enhanced image frame based on the multi-scale feature map of the image frame to be processed.

[0079] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0080] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0081] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0082] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A digital intermediate efficient processing and editing technology system, characterized by: include: A digital intermediate import and analysis module, used for importing an original digital intermediate and analyzing the content of the original digital intermediate; an efficient processing and editing module, for editing, synthesizing and modifying the original digital intermediate to obtain an edited digital intermediate, wherein the efficient processing and editing module integrates a high-performance processing engine; an image enhancement module, configured to perform image enhancement on the edited digital intermediate to obtain an enhanced digital intermediate, comprising: a digital intermediate acquisition unit configured to acquire the edited digital intermediate; a to-be-processed image frame extraction unit configured to extract to-be-processed image frames from the edited digital intermediate; a multi-level image feature extraction unit configured to extract multi-level image features of the to-be-processed image frames to obtain a to-be-processed image frame color-level feature map and a to-be-processed image frame texture-level feature map; a fusion unit configured to fuse the to-be-processed image frame color-level feature map and the to-be-processed image frame texture-level feature map to obtain a to-be-processed image frame multi-scale feature map; and an enhanced image frame generation unit configured to generate an enhanced image frame based on the to-be-processed image frame multi-scale feature map, wherein the enhanced digital intermediate includes the enhanced image frame; a data security management module, configured to perform data encryption processing on the enhanced digital intermediate using a data encryption technology to obtain a processed digital intermediate; and a multi-platform compatibility and export module for exporting the processed digital intermediate.

2. The digital intermediate efficient processing and editing technology system according to claim 1, characterized in that: The multi-level image feature extraction unit includes: A color feature extraction subunit is configured to perform feature extraction on the image frame to be processed using a first deep learning network model to obtain a color-level feature map of the image frame to be processed; And a texture feature extraction subunit, which is used to use a second deep learning network model to extract features of the image frame to be processed to obtain a texture-level feature map of the image frame to be processed.

3. The digital intermediate efficient processing and editing technology system according to claim 2, characterized in that: The first deep learning network model is a color feature extractor based on a first convolutional neural network model, and the second deep learning network model is a texture feature extractor based on a second convolutional neural network model.

4. The digital intermediate efficient processing and editing technology system according to claim 3, characterized in that: The color feature extractor of the first convolutional neural network model comprises: a first input layer, a first convolutional layer, a first pooling layer, a first activation layer and a first output layer; The texture feature extractor based on the second convolutional neural network model includes: a second input layer, a second convolutional layer, a second pooling layer, a second activation layer and a second output layer.

5. The digital intermediate efficient processing and editing technology system according to claim 4, characterized in that: The color feature extraction subunit is used to: The image frame to be processed is passed through the color feature extractor based on the first convolutional neural network model to obtain a color-level feature map of the image frame to be processed.

6. The digital intermediate efficient processing and editing technology system according to claim 5, characterized in that: The texture feature extraction subunit is used to: The color-level feature map of the image frame to be processed is passed through the texture feature extractor based on the second convolutional neural network model to obtain the texture-level feature map of the image frame to be processed.

7. The digital intermediate efficient processing and editing technology system according to claim 6, characterized in that: The fusion unit comprises: An upsampling and convolution processing subunit, configured to perform upsampling and convolution processing on the texture-level feature map of the image frame to be processed to obtain a reconstructed texture-level feature map of the image frame to be processed; A position difference calculation subunit is used to calculate the position difference between the reconstructed texture-level feature map of the image frame to be processed and the color-level feature map of the image frame to be processed to obtain a difference feature map; a nonlinear activation processing subunit, configured to perform nonlinear activation processing based on a Sigmoid function on the difference feature map to obtain a mask feature map; a feature map calculation subunit, configured to perform a dot multiplication on the color-level feature map of the image frame to be processed and the mask feature map to obtain a fused feature map; and The pooling operation subunit is used to perform an attention-based PMA pooling operation on the fused feature map to obtain a multi-scale feature map of the image frame to be processed.

8. The digital intermediate efficient processing and editing technology system according to claim 7, characterized in that: The enhanced image frame generating unit includes: an optimization subunit, configured to optimize the multi-scale feature map of the image frame to be processed to obtain an optimized multi-scale feature map of the image frame to be processed; and an image enhancement subunit, configured to pass the optimized multi-scale feature map of the image frame to be processed through an image enhancer based on a generative adversarial network to obtain the enhanced image frame.

Images