Abstract: Automatic creation of product photoshoots that seamlessly combine real-life image portions and Artificial Intelligence (AI) generated portions. An input photograph of a product is received, and a background-removed version is generated. The image is fed into a Vision and Language Model (VLM) that generates textual attributes that pertain to the product. The textual attributes are fed into a Large Language Model (LLM), that generates a proposed textual prompt that will command an Image Diffusion unit to generate via Generative AI an image of a scenery that would be appropriate for showcasing that product. The AI-generated image is fed into an AI-based unit that detects and cures visual abnormalities or visual distortions, with AI-based blending and refinement. The method generates a high-definition abnormality-free and distortion-free output image that depicts the real-world product blended seamlessly within the Generative-AI scenery.

Abstract: System, device, and method for improved image encoding that non-iteratively targets and achieves a visual-quality threshold and a compression efficiency threshold. A system receives an input image intended for compression; and applies a pre-trained Convolutional Neural Network (CNN) Engine to predict what would be (i) compression ratio values and (ii) visual-quality score values, for a resulting compressed image that would be encoded or transcoded from said input image, if said input image would be compressed via a particular Image Encoder from a pool of available Image Encoders by using a particular Encoder Settings from a pool of Image Encoder Settings. Based on CNN-based predictions generated by the pre-trained CNN Engine, the system selects a single combination of Image Encoder with Encoder Setting, and performs a single compression of said input image using said single combination of Image Encoder with Encoder Setting to generate an optimally compressed image.

Abstract: System, device, and method for improved image encoding that non-iteratively targets and achieves a visual-quality threshold and a compression efficiency threshold. A system receives an input image intended for compression; and applies a pre-trained Convolutional Neural Network (CNN) Engine to predict what would be (i) compression ratio values and (ii) visual-quality score values, for a resulting compressed image that would be encoded or transcoded from said input image, if said input image would be compressed via a particular Image Encoder from a pool of available Image Encoders by using a particular Encoder Settings from a pool of Image Encoder Settings. Based on CNN-based predictions generated by the pre-trained CNN Engine, the system selects a single combination of Image Encoder with Encoder Setting, and performs a single compression of said input image using said single combination of Image Encoder with Encoder Setting to generate an optimally compressed image.

Abstract: System, device, and method for improved encoding and enhanced compression of images, videos, and media content. A method includes: (a) receiving a source image, and analyzing its content on a pixel-by-pixel basis, and classifying each pixel as either (I) a pixel associated with Photographic content, or (II) a pixel associated with Non-Photographic content; (c) generating a pixel-clusters map that indicates (i) clusters of pixels that were classified as Photographic content, and (ii) clusters of pixels that were classified as Non-Photographic content; (d) generating a composed image, by: (d1) applying a first encoding technique, particularly lossy encoding, to encode pixel-clusters that were classified as Photographic content; (d2) applying a second, different, encoding technique, particularly lossless encoding, to encode pixel-clusters that were classified as Non-Photographic content.

Abstract: System, device, and method for improved encoding and enhanced compression of images, videos, and media content. A method includes: (a) receiving a source image, and analyzing its content on a pixel-by-pixel basis, and classifying each pixel as either (I) a pixel associated with Photographic content, or (II) a pixel associated with Non-Photographic content; (c) generating a pixel-clusters map that indicates (i) clusters of pixels that were classified as Photographic content, and (ii) clusters of pixels that were classified as Non-Photographic content; (d) generating a composed image, by: (d1) applying a first encoding technique, particularly lossy encoding, to encode pixel-clusters that were classified as Photographic content; (d2) applying a second, different, encoding technique, particularly lossless encoding, to encode pixel-clusters that were classified as Non-Photographic content.

Abstract: Systems, devices, and methods for determining color ambiguity of images or videos. A system includes a color ambiguity score generator, which analyzes an image and determines a color ambiguity score that quantitively indicates a level of color ambiguity that the image is estimated to cause when viewed by a user having color vision deficiency. A local color ambiguity score is generated to quantitively indicate a level of local color ambiguity between (i) an in-image object and (ii) an in-image foreground of that in-image object. A global color ambiguity score is generated to quantitively indicate a level of global color ambiguity between (I) a first in-image object within the image and (II) a second in-image object within that image. The color ambiguity score generator generates the color ambiguity score by utilizing a formula that uses both (A) the local color ambiguity score and (B) the global color ambiguity score.

Abstract: System, device, and method for generating and utilizing content-aware metadata, particularly for playback of video and other content items. A method includes: receiving a video file, and receiving content-aware metadata about visual objects that are depicted in said video file; and dynamically adjusting or modifying playback of that video file, on a video playback device, based on the content-aware metadata. The modifications include content-aware cropping, summarizing, watermarking, overlaying of other content elements, modifying playback speed, adding user-selectable indicators or areas around or near visual objects to cause a pre-defined action upon user selection, or other adjustments or modification. Optionally, a modified and content-aware version of the video file is automatically generated or stored. Optionally, the content-aware metadata is stored internally or integrally within the video file, in its header or as a private channel; or is stored in an accompanying file.

Abstract: System, device, and method of providing authenticity and rights verification mechanism for media content and for its derived versions. A media authenticity server is configured to receive a content item, and to generate for it a record having a unique content identifier and indications of permitted modifications, and optionally also copyright information usage restrictions. The media authenticity server authorizes or blocks modifications requests regarding the content item. The media authenticity server tracks and logs the permitted modifications performed on the content item, and makes this log available for inspection to end-user devices via a web browser or via a content consumption application. Optionally, playback or consumption of a modified version of the content item is blocked, or is accompanied by a warning message, if the modified version is not associated with an authenticated log of permitted modifications.

Abstract: System, device, and method for enabling high-quality content-aware zoom-in for videos. An input video is received at high resolution, and is processed. A first video stream is generated, being a downscaled lower-resolution version of the input video. One or more additional video streams are generated; each one of them being a cropped high-resolution version of the input video, such that the cropped region tracks an object-of-interest that is visually depicted in the input video. A multiple-streams manifest is generated, pointing to the first, downscaled, video stream, and also pointing to the one or more other, cropped high-resolution video stream. An end-user device plays the video, and enables the end-user to perform a high-quality zoom-in on the object-of-interest, by transitioning from playback of the downscaled video stream to playback of the additional video stream that tracks that object-of-interest.

Abstract: System, device, and method for generating and utilizing content-aware metadata, particularly for playback of video and other content items. A method includes: receiving a video file, and receiving content-aware metadata about visual objects that are depicted in said video file; and dynamically adjusting or modifying playback of that video file, on a video playback device, based on the content-aware metadata. The modifications include content-aware cropping, summarizing, watermarking, overlaying of other content elements, modifying playback speed, adding user-selectable indicators or areas around or near visual objects to cause a pre-defined action upon user selection, or other adjustments or modification. Optionally, a modified and content-aware version of the video file is automatically generated or stored. Optionally, the content-aware metadata is stored internally or integrally within the video file, in its header or as a private channel; or is stored in an accompanying file.

Abstract: System, device, and method of providing authenticity and rights verification mechanism for media content and for its derived versions. A media authenticity server is configured to receive a content item, and to generate for it a record having a unique content identifier and indications of permitted modifications, and optionally also copyright information usage restrictions. The media authenticity server authorizes or blocks modifications requests regarding the content item. The media authenticity server tracks and logs the permitted modifications performed on the content item, and makes this log available for inspection to end-user devices via a web browser or via a content consumption application. Optionally, playback or consumption of a modified version of the content item is blocked, or is accompanied by a warning message, if the modified version is not associated with an authenticated log of permitted modifications.

Abstract: Systems, devices, and methods for determining color ambiguity of images or videos. A system includes a color ambiguity score generator, which analyzes an image and determines a color ambiguity score that quantitively indicates a level of color ambiguity that the image is estimated to cause when viewed by a user having color vision deficiency. A local color ambiguity score is generated to quantitively indicate a level of local color ambiguity between (i) an in-image object and (ii) an in-image foreground of that in-image object. A global color ambiguity score is generated to quantitively indicate a level of global color ambiguity between (I) a first in-image object within the image and (II) a second in-image object within that image. The color ambiguity score generator generates the color ambiguity score by utilizing a formula that uses both (A) the local color ambiguity score and (B) the global color ambiguity score.

Abstract: System, device, and method for determining predicted annoyance level of multimedia content. A Predicted Annoyance Level (PAL) management unit processes content of frames of an endless-loop cinemagraph content-item, and determines a PAL score that indicates a predicted level of annoyance that the content-item is predicted to cause viewers due to motion of content in its content-item. The determining of the PAL score is based on comparison of content of pairs of frames of the endless-loop cinemagraph content-item; content of each frame of is compared to content of its immediately preceding frame, except for the first frame of the endless-loop cinemagraph content item which is compared to the last frame thereof. The PAL score is adjusted based on time-length and frame rate of the content item. Analysis of the PAL score triggers one or more notifications, alerts, or mitigation operations.

Abstract: An aspect of the present invention includes a method and device for translating a structured message into a context tagged, XML message. A two step translation is described, first including translation from structured message to structure tagged message, and then from structure tagged message to context tagged message. Standard tools may be used in the second step to apply validations or business logic. Particular aspects of the present invention are described in the claims, specification and drawings.

Abstract: An aspect of the present invention includes a method and device for translating a structured message into a context tagged, XML message. A two step translation is described, first including translation from structured message to structure tagged message, and then from structure tagged message to context tagged message. Standard tools may be used in the second step to apply validations or business logic. Particular aspects of the present invention are described in the claims, specification and drawings.

Abstract: An aspect of the present invention includes a method and device for translating a structured message into a context tagged, XML message. A two step translation is described, first including translation from structured message to structure tagged message, and then from structure tagged message to context tagged message. Standard tools may be used in the second step to apply validations or business logic. Particular aspects of the present invention are described in the claims, specification and drawings.