Abstract: A system comprises a source block buffer and a plurality of hardware motion estimation search processing units in communication with the source block buffer. The source block buffer is configured to store at least a portion of a source block of a source frame of a video. The plurality of hardware motion estimation search processing units are configured to perform at least a portion of a motion estimation for the source block at least in part in parallel across a plurality of different reference frames of the video.

Abstract: A disclosed computer-implemented method may include (1) selecting, from a video stream, a reference frame and a current frame, (2) collecting a reference histogram of the reference frame and a current histogram of the current frame, and (3) generating a smoothed reference histogram by applying a smoothing function to at least a portion of the reference histogram. In some examples, the computer-implemented method may also include (1) determining a similarity metric between the smoothed reference histogram and the current histogram and, (2) when the similarity metric is greater than a threshold value, applying weighted prediction during a motion estimation portion of an encoding of the video stream. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system comprises a memory storage configured to store at least a portion of a frame of a video and a hardware motion estimation search processing unit configured to perform at least a portion of a motion estimation search for the video for a plurality of different block sizes. The hardware motion estimation search processing unit is configured to perform the motion estimation search using a plurality of source sub-blocks of a first block size to determine a first type of comparison evaluation values for the first block size. A combination of values included in the first type of comparison evaluation values is utilized to determine at least one second type of comparison evaluation values for a second block size, wherein the second block size is larger than the first block size.

Abstract: A disclosed computer-implemented method may include (1) selecting, from a video stream, a reference frame and a current frame, (2) collecting a reference histogram of the reference frame and a current histogram of the current frame, and (3) generating a smoothed reference histogram by applying a smoothing function to at least a portion of the reference histogram. In some examples, the computer-implemented method may also include (1) determining a similarity metric between the smoothed reference histogram and the current histogram and, (2) when the similarity metric is greater than a threshold value, applying weighted prediction during a motion estimation portion of an encoding of the video stream. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system comprises a source block buffer and a plurality of hardware motion estimation search processing units in communication with the source block buffer. The source block buffer is configured to store at least a portion of a source block of a source frame of a video. The plurality of hardware motion estimation search processing units are configured to perform at least a portion of a motion estimation for the source block at least in part in parallel across a plurality of different reference frames of the video. Each of the hardware motion estimation search processing units is configured to be assigned a different one of the plurality of different reference frames and is configured to compare at least the portion of the source block with a portion of the assigned one of the different reference frames.

Abstract: The disclosed may include various systems and methods for improving the efficiency and scalability of large-scale systems. For example, the disclosed may include systems and methods for automatic privacy enforcement using privacy-aware infrastructure, scalable general-purpose low cost integer motion search, efficient scaler filter coefficients layout for flexible scaling quality control with limited hardware resources, hardware optimization for power saving with both different codecs enabled, optimizing storage overhead and performance for large distributed data warehouse, mass and volume efficient integration of intersatellite link terminals to a satellite bus, and overcoming retention limit for memory-based distributed database systems.

Abstract: Methods, systems, and devices for object detection are described. A device may extract features from an image and identify a region within the image for object detection. The device may apply an object model to a first set of features corresponding to positions within the region and one or more channels of the image. The first set of features may be selected so that the object model can detect a first orientation of an object. The device may also apply the object model to a second set of features, from the region, that are different from the first set of features. The second set of features may be selected so that the object model can detect a second orientation of the object (e.g., a flipped or rotated orientation of the object with respect to the first orientation).

Abstract: Methods, systems, and devices for object detection are described. A device may extract features from an image and identify a region within the image for object detection. The device may apply an object model to a first set of features corresponding to positions within the region and one or more channels of the image. The first set of features may be selected so that the object model can detect a first orientation of an object. The device may also apply the object model to a second set of features, from the region, that are different from the first set of features. The second set of features may be selected so that the object model can detect a second orientation of the object (e.g., a flipped or rotated orientation of the object with respect to the first orientation).

Abstract: A classifier for detecting objects in images can be configured to receive features of an image from a feature extractor. The classifier can determine a feature window based on the received features, and allows access by each decision tree of the classifier to only a predetermined area of the feature window. Each decision tree of the classifier can compare a corresponding predetermined area of the feature window with one or more thresholds. The classifier can determine an object in the image based on the comparisons. In some examples, the classifier can determine objects in a feature window based on received features, where the received features are based on color information for an image.

Abstract: A method performed by an electronic device is described. The method includes obtaining a motion vector map based on at least two images. The motion vector map has fewer motion vectors than a number of pixels in each of the at least two images. The method also includes obtaining a feature point from one of the at least two images. The method further includes performing a matching operation between a template associated with the feature point and at least one search space based on the motion vector map. The method additionally includes determining a motion vector corresponding to the feature point based on the matching operation.

Abstract: A method for determining a histogram is described. The method includes storing a plurality of histograms in a memory bank, each histogram corresponding to a group of pixels in a region of interest of an image. The method also includes initiating transfer of one or more histograms between the memory bank and a local memory. The method further includes finding, for an incoming pixel, weights of each bin for the one or more histograms stored in the local memory. The method additionally includes adding the weights to the one or more histograms stored in the local memory. The method also includes transferring one or more updated histograms from the local memory to the memory bank. The method further includes replacing a corresponding one or more histograms in the memory bank with the one or more updated histograms.

Abstract: A method performed by an electronic device is described. The method includes obtaining a motion vector map based on at least two images. The motion vector map has fewer motion vectors than a number of pixels in each of the at least two images. The method also includes obtaining a feature point from one of the at least two images. The method further includes performing a matching operation between a template associated with the feature point and at least one search space based on the motion vector map. The method additionally includes determining a motion vector corresponding to the feature point based on the matching operation.

Abstract: Method and apparatus for reducing random noise in digital video streams are described. In one innovative aspect, the device includes a noise estimator. The device also includes a motion detector configured to determine a motion value indicative of motion between two frames of the video stream, the motion value based at least in part on the noise value. The device further includes a spatial noise reducer configured to filter the image data based at least in part on a blending factor and the noise value. The device also includes a temporal noise reducer configured to filter the video data based on the motion value and the noise value. The device also includes a blender configured to blend the spatial and temporal filtered values to provide a weighted composite filtered output image.

Abstract: Systems and methods are provided for determining a characteristic of video data. A set of N frames of the video data is obtained and filtered using at least one filter to produce a set of N×T blocks of filtered video data, where T is a partition size associated with the at least one filter. Each block in the set of N×T blocks is classified as either a first type block or a second type block. A subset of blocks in the set of N×T blocks is associated with a corresponding frame from the set of N frames. The characteristic of video data is determined based, at least in part, on the subset of blocks in the set of N×T blocks that are associated with the frame.

Abstract: Systems and methods for improving the contrast of image frames are disclosed. In one embodiment, a system for improving the contrast of image frames includes a control module configured to create an intensity histogram for an image frame, define a set of markers on an intensity range of the histogram, assign a blend factor to each marker, calculate a blend factor for each original pixel of the image, obtain a first equalized pixel output value, calculate a final equalized pixel output value using the blend factor, the first equalized pixel output value, and an original pixel value, and output new pixel values that constitute the output image.

Abstract: The adaptive contrast enhancer uses an adaptive histogram equalization-based approach to improve contrast in a video signal. For each video frame, the histogram of the pixel luminance values is calculated. The calculated histogram is divided into three regions that are equalized independently of the other. The equalized values are averaged with the original pixel values with a weighting factor that depends on the shape of the histogram. The weighting factors can be also chosen differently for the three regions to enhance the darker regions more than the brighter ones. The statistics calculated from one frame are used to enhance the next frame such that frame buffers are not required. Many of the calculations are done in the inactive time between two frames.

Abstract: Devices, systems, methods, and other embodiments associated with reducing digital image noise are described. In one embodiment, a method includes filtering a digital image with a plurality of adaptive filters, wherein the plurality of adaptive filters include a first filter configured to filter noise surrounding one or more edges in the digital image, and a second filter configured to filter noise caused by a block based encoding of the digital image. The method further includes reducing a compression artifact from selected pixels in the digital image, wherein the compression artifact is reduced by (i) combining an output from the first filter and an output from the second filter in response to the digital image being determined to be blocky, and (ii) not combining the output from the first filter with the output of the second filter in response to the digital image not being determined to be blocky.

Abstract: Systems and methods are provided for determining characteristics of video data. A frame of video data is obtained, where the frame is represented by pixel data. A value is assigned to an element of a detection array based on pixel data in a portion of the video frame corresponding to the element. A frequency transform of values of the detection array is determined, and a characteristic of the video data is extracted based on the output of the frequency transform.

Abstract: Devices, systems, apparatuses, methods, and other embodiments associated with bit resolution enhancement are described. In one embodiment, an apparatus includes logic configured to produce a high-resolution pixel from a low-resolution pixel. The apparatus includes logic configured to classify the high-resolution pixel as being in a smooth region of an image based on at least one of a gradient value and a variance value associated with the low-resolution pixel. The apparatus includes logic configured to selectively re-classify the high-resolution pixel as not being in the smooth region of the image based on a set of neighboring high-resolution pixels associated with high-resolution pixel. The apparatus includes logic configured to selectively filter the high-resolution pixel based on whether the high-resolution pixel remains classified as being in the smooth region of the image.

Abstract: Systems and methods are provided for detecting sharpness among video fields. In certain implementations of the systems and methods, a plurality of video fields is received and a sharpness metric for each of the plurality of video fields is determined. The sharpness metric of a first video field is compared to the sharpness metric of a second video field among the plurality of video fields and a video field source of the first video field and the second video field is determined based on the comparison.