Marvell International Limited

Marvellworld Trade Ltd.

Qualcomm Incorporated

Sujith Srinivasan

Nikhil Balram

Sanjay Saran Garg

Vasudev Bhaskaran

Mainak Biswas

Byas Muni

Vipin Namboodiri

Shilpi Sahu

Bipasha Ghosh

Gokce Dane

Rakesh Nattoji Rajaram

Kaustubh Milind Patankar

Panikumar Gururaj Kallamballe

Ashish Kejriwal

Biswas Mainak

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).

Symmetry exploitation for object detection in images

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.

Systems and methods for determining feature point motion

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.

Method and apparatus of reducing random noise in digital video streams

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&#xd7;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&#xd7;T blocks is classified as either a first type block or a second type block. A subset of blocks in the set of N&#xd7;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&#xd7;T blocks that are associated with the frame.

Noise estimation based on a characteristic of digital video

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.

Systems and methods for localized contrast enhancement

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.

Adaptive histogram-based video contrast enhancement

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.

Method and apparatus for reducing noise introduced into a digital image by a video compression encoder

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.

Block noise detection in digital video

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.

Bit resolution enhancement

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.

Systems and methods for determining video field sharpness

System and methods for gamut bounded saturation adaptive color enhancement are provided. Color enhancement incorporating gamut bounded saturation enhances colors of an pixel from a source color gamut such that the resulting color is within a target color gamut. This resulting color may, for example, take advantage of an expanded target color gamut of a display. Gamut bounded saturation may be implemented independently or in combination with RGB bounded saturation.

System and methods for gamut bounded saturation adaptive color enhancement

Devices, systems, methods, and other embodiments associated with reducing digital image noise are described. In one embodiment, a method includes determining, on a per pixel basis, mosquito noise values associated with pixels of a digital image. The method determines, on a per pixel basis, block noise values associated with the digital image. The method filters the digital image with a plurality of adaptive filters. A compression artifact in the digital image is reduced. The compression artifact is reduced by combining filter outputs from the plurality of adaptive filters. The filter outputs are combined based, at least in part, on the mosquito noise values and the block noise values.

Reducing digital image noise

Systems and methods for converting a picture frame rate from a source video at a first rate to a target video at a second rate via interpolation of an intermediate frame. In one implementation, the system includes a phase plane correlation calculator including a low pass filter and a high pass filter for receiving previous frame data and current frame data where the phase plane correlation calculator is configured to generate a first motion vector based upon low pass representations and high pass representations. The system may also include a motion compensated interpolator that receives the first motion vector and an additional input motion vector and determines a final motion vector for use in interpolation. The system may further include an intermediate frame generator configured to generate the intermediate frame utilizing the final motion vector.

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Bengaluru, India

Sujith Srinivasan