Abstract: A multi-frame video analytics (MFVA) system is disclosed, in which contiguous video frame-sequences are processed in reconstruction of 3D dynamical relationships amongst tracked objects. A tensor-based spatiotemporal volume (STV) formalism mapped to buffered successive video-frames is employed. An optical flow field calculation is then applied to this STV for recovery of z-axis information. Standard dynamics in terms of position, velocity, and acceleration are then reconstructed via numerical approximation of time-derivatives on the set of spatiotemporal trajectories (STT) generated by tracked objects that together constitute a bounding STV. In this manner, STVs are rendered causal, with implication application of a suitable knowledge (KBASE) representation also generates an explanatory framework consisting of a set of primitive assertions on tracked objects, now informed by dynamical state, phase flow, and interaction events.

Abstract: Systems and methods for transmitting source video over a bandwidth-limited network. First, high frequency spectral content of the video is encoded into layer-1 data files. In parallel, frames of the video are downsampled and compressed using a lossy CODEC, to define layer-2 data files with high-frequency spectral content removed therefrom. The layer-1 and layer-2 data files are interleaved and are smaller than conventional lossy CODEC compressed files. After receipt, the data files are deinterleaved. The layer-2 data is decompressed and upsampled to create a preliminary reconstructed video. The high frequency spectral content is extracted from the layer-1 data files, and photometric warp superresolution processing restores the high frequency spectral content into the preliminary reconstructed video to generate a final, output video at resolution at or near the source video. An adaptive spatiotemporal noise filtering is applied to each frame of the source video to improve sharpness of the output video.

Abstract: Systems, processes, devices, and implementing technologies for organizing, searching, analyzing, and retrieving previously unstructured data obtained from one or more databases of voluminous unstructured data (“BigData”), including data in video/image/audio (VIA data) format. Where analysis of BigData is considered, there are preferably three component processes comprising any complete BidData Software Solution including (i) an organization component or processes, termed BigData Assembly (BDA), (ii) an exploration component or processes, termed BigData Exploration (BDE), and (iii) a discovery component or processes, termed BigData Discovery (BDD). Superresolution-enabled (SRE) video processing is used to provide structure and accessibility to such VIA data.

Abstract: Systems and methods for transmitting source video (305) over a bandwidth-limited network (350). First, high frequency spectral content of the video is encoded into layer-1 data files (330). In parallel, frames of the video are downsampled (342) and compressed (333) using a lossy CODEC, to define layer-2 data files (335a, 335b) with high-frequency spectral content removed therefrom. The layer-1 and layer-2 data files are interleaved (345a, 345b) and transmitted over the network and are smaller than conventional lossy CODEC compressed files. After receipt, the layer-1 and layer-2 data files are deinterleaved. The layer-2 data is decompressed (353) and upsampled (362) to create a preliminary reconstructed video.

Abstract: Methods, systems, devices, and implementing technologies for up-scaling an original source video from a lower, first resolution to a desired output video having a higher, second resolution, using fractal zooming techniques to replace individual source pixels in each respective image frame of the original source video with a plurality of proposed replacement pixels in the vertical and horizontal dimensions having similar characteristics as the individual source pixel, reducing noise associated with each respective frame of the desired output video, re-sizing, as necessary, each respective replacement frame to the second resolution, and outputting each zoomed replacement frame to generate the desired output video, which is an up-scaled version of the original source video. The quality of the up-scaled output video is improved by using artifact filtering and linear smoothing techniques after the initial search and replace steps of the fractal zooming process.

Abstract: A codec, systems, and methods for compressing video data includes selecting a current video frame of the video data, dividing the current video frame into multiple blocks, wherein each block has M×N pixels, approximating the blocks of the current frame based on motion vectors associated with corresponding blocks from the previous frame, further refining the blocks of the current frame by determining optimal motion vectors for the blocks of the current frame based on the motion vectors provided (i) by the corresponding blocks from the previous frame, (ii) by the surrounding blocks in the current frame, and (iii) from successively smaller blocks within the current frame, wherein the optimal motion vectors for the blocks of the current frame are optimized to balance distortion and rate and wherein the optimal motion vectors are represented by codewords generated from Huffman tables.

Abstract: Methods, systems, and devices for up-scaling a source input video from a lower, first resolution to a desired output video having a higher, second resolution, using fractal zooming techniques to replace each individual source pixel of each respective frame of the source input video with a multiple of proposed replacement pixels in the vertical and horizontal dimensions having similar characteristics as the individual source pixel, reducing noise associated with each respective frame of the desired output video, re-sizing, as necessary, each respective replacement frame to the second resolution, and outputting each zoomed replacement frame to generate the desired output video having a higher, second resolution, which is the up-scaled version of the source video. The fractal zooming techniques include identifying a plurality of candidate pixels from the source video and selecting a group of pixels from the candidate pixels that best matches the individual source pixel.

Abstract: A computerized system for up-scaling a source input video from a lower, first resolution to a desired output video having a higher, second resolution, using fractal zooming techniques to replace each individual source pixel of each respective frame of the source input video with a multiple of proposed replacement pixels in the vertical and horizontal dimensions having similar characteristics as the individual source pixel, converting each of the proposed replacement pixels into a desired color space for the desired output video, using low-pass filtering to reduce noise associated with each respective frame of the desired output video, down-sizing the output video to its desired resolution, and outputting each zoomed replacement frame to generate the desired output video.

Abstract: A computerized system for up-scaling a source input video from a lower, first resolution to a desired output video having a higher, second resolution, using fractal zooming techniques to replace each individual source pixel of each respective frame of the source input video with a multiple of proposed replacement pixels in the vertical and horizontal dimensions having similar characteristics as the individual source pixel, converting each of the proposed replacement pixels into a desired color space for the desired output video, using low-pass filtering to reduce noise associated with each respective frame of the desired output video, down-sizing the output video to its desired resolution, and outputting each zoomed replacement frame to generate the desired output video.

Abstract: A codec, systems, and methods for compressing video data includes selecting a current video frame of the video data, dividing the current video frame into multiple blocks, wherein each block has M×N pixels, approximating the blocks of the current frame based on motion vectors associated with corresponding blocks from the previous frame, further refining the blocks of the current frame by determining optimal motion vectors for the blocks of the current frame based on the motion vectors provided (i) by the corresponding blocks from the previous frame, (ii) by the surrounding blocks in the current frame, and (iii) from successively smaller blocks within the current frame, wherein the optimal motion vectors for the blocks of the current frame are optimized to balance distortion and rate and wherein the optimal motion vectors are represented by codewords generated from Huffman tables.