Abstract: There are provided computerized systems and methods for detection of objects in a one or more images or video frames, which require reduced power or computational cost compared to existing methods. The proposed approach uses a set of per frame detection anchors which are split into a primary sub-set and one or more secondary sub-sets. For each key frame a full detection process for each of the detection anchors in primary subset is performed, determining for each detection anchor whether it corresponds to a detected object, and updating a detected object data structure accordingly. For each non ‘key-frame’, a history based fast detection process for each of the detection anchors in the primary subset is performed, where it is first determined whether a criterion for temporal based early exit is met and whether the detection anchor corresponds to a detected object and updating the detected object data structure according to detection results.

Abstract: There are provided computerized systems and methods of pre-processing video information for optimized video encoding. In various aspects, the pre-processing can be performed in different ways. By way of example, this can be performed by aligning a plurality of consecutive frames in the sequence of video frames. By way of another example, this can be performed by conducting adaptive pre-filtering of the video information. By way of yet another example, this can be performed by filtering the video information in order to remove grain content thereof.

Abstract: There are provided computerized systems and methods of pre-processing video information for optimized video encoding. In various aspects, the pre-processing can be performed in different ways. By way of example, this can be performed by aligning a plurality of consecutive frames in the sequence of video frames. By way of another example, this can be performed by conducting adaptive pre-filtering of the video information. By way of yet another example, this can be performed by filtering the video information in order to remove grain content thereof.

Abstract: There are provided a system and method of classification of an object represented by input data comprising a plurality of components. The method includes obtaining a restructured tree corresponding to a decision tree, providing appropriate components selected from the plurality of components to corresponding duplicated non-leaf nodes of each given layer to be used in duplicated condition associated thereof; for each given layer of the restructured tree that comprises duplicated non-leaf nodes, generating a per layer output by applying the duplicated condition associated with each duplicated non-leaf node on corresponding appropriate component, and applying an inversion mask to the per layer output to obtain a corrected per layer output, giving rise to a plurality of corrected per layer outputs, and combining the plurality of corrected per layer outputs giving rise to the classification of the object.

Abstract: There is provided a computerized method and system of controlling a quality measure in a compression quality evaluation system, the method comprising: calculating a grain value indicative of an extent of grain present in an input image, the grain value being calculated based on one or more features characterizing a base image related to the input image; and configuring the quality measure upon a criterion being met by the value, the quality measure being indicative of perceptual quality of a compressed image compressed from the input image. The calculated grain value may be dependent also on further characteristics of the input image, or in the case of a sequence of images, dependent also on the relation between the image and the preceding image.

Abstract: There is provided a computerized method and system of controlling a quality measure in a compression quality evaluation system, the method comprising: calculating a grain value indicative of an extent of grain present in an input image, the grain value being calculated based on one or more features characterizing a base image related to the input image; and configuring the quality measure upon a criterion being met by the value, the quality measure being indicative of perceptual quality of a compressed image compressed from the input image. The calculated grain value may be dependent also on further characteristics of the input image, or in the case of a sequence of images, dependent also on the relation between the image and the preceding image.