Abstract: A method of performing image signal processing includes: determining whether at least one neighbor pixel is available for each of a plurality of current pixels in a current block, wherein each of the plurality of current pixels has a current pixel value; estimating a predict pixel value for each of the plurality of current pixels in the current block based on a pixel value of the at least one neighbor pixel corresponding to each of the plurality of current pixels, using at least one of a plurality of predefined prediction modes; determining a difference metric between the predict pixel value and the current pixel value for each of the plurality of current pixels; and obtaining a processed pixel value for each of the plurality of current pixels based on the difference metric.

Abstract: A method for handling a fixed bound calibration data in an electronic device includes: determining, by the electronic device, a calibration data from a calibration setup; and generating and encoding, by the electronic device, a fixed bound calibration data based on the determined calibration data.

Abstract: A method of performing image signal processing includes: determining whether at least one neighbor pixel is available for each of a plurality of current pixels in a current block, wherein each of the plurality of current pixels has a current pixel value; estimating a predict pixel value for each of the plurality of current pixels in the current block based on a pixel value of the at least one neighbor pixel corresponding to each of the plurality of current pixels, using at least one of a plurality of predefined prediction modes; determining a difference metric between the predict pixel value and the current pixel value for each of the plurality of current pixels; and obtaining a processed pixel value for each of the plurality of current pixels based on the difference metric.

Abstract: A method for handling a fixed bound calibration data in an electronic device includes: determining, by the electronic device, a calibration data from a calibration setup; and generating and encoding, by the electronic device, a fixed bound calibration data based on the determined calibration data.

Abstract: An imaging system and method compensate lens distortion for an image. The system identifies lens distortion among one or more distortions, and extracts a Point Spread Function (PSF) for the lens distortion. Additionally, the system extracts a total PSF associated with total distortion in the input image and separates a PSF for the lens distortion from the total PSF. The system updates the pre-estimated kernel with the PSF of the lens distortion when a rank of the PSF matches with the predefined rank value, and one or more difference values determined between one or more singular values of the PSF and one or more singular values of the pre-estimated kernel is within a pre-defined threshold limit. Thus, quality of each output image obtained from the system may be maintained irrespective of error introduced by wear and tear on the lens of the system over a period of time.

Abstract: An imaging system and method compensate lens distortion for an image. The system identifies lens distortion among one or more distortions, and extracts a Point Spread Function (PSF) for the lens distortion. Additionally, the system extracts a total PSF associated with total distortion in the input image and separates a PSF for the lens distortion from the total PSF. The system updates the pre-estimated kernel with the PSF of the lens distortion when a rank of the PSF matches with the predefined rank value, and one or more difference values determined between one or more singular values of the PSF and one or more singular values of the pre-estimated kernel is within a pre-defined threshold limit. Thus, quality of each output image obtained from the system may be maintained irrespective of error introduced by wear and tear on the lens of the system over a period of time.

Abstract: Provided is a method of motion estimation for processing a video stream comprising a plurality of frames, the method including segmenting at least one frame, from among the plurality of frames, into a plurality of blocks, determining an event density factor for each block included in a frame, wherein the event density factor of the block corresponds to a number of events accumulated in the block across frames in a predetermined time duration, comparing the determined event density factor with a threshold value, estimating a motion vector of the block based on the comparison, and processing the block in the video stream based on the estimated motion vector of the block.

Abstract: Embodiments herein provide methods and systems for providing selective disparity refinement. The embodiments include detecting objects of interest of type similar to a type of a point of interest in an image. Disparity values may be determined for the point of interest and the objects of interest in the captured image. Objects of interest may be selected with disparity values within a disparity value threshold range, wherein the disparity value threshold range is based on disparity value of the point of interest. The embodiments include performing a redistribution of disparity values of objects in the image with disparity values not within the disparity value threshold range. The embodiments include assigning a disparity value to the selected objects of interest based on the disparity value of the point of interest, to achieve selective disparity refinement.

Abstract: Embodiments herein provide methods and systems for providing selective disparity refinement. The embodiments include detecting objects of interest of type similar to a type of a point of interest in an image. Disparity values may be determined for the point of interest and the objects of interest in the captured image. Objects of interest may be selected with disparity values within a disparity value threshold range, wherein the disparity value threshold range is based on disparity value of the point of interest. The embodiments include performing a redistribution of disparity values of objects in the image with disparity values not within the disparity value threshold range. The embodiments include assigning a disparity value to the selected objects of interest based on the disparity value of the point of interest, to achieve selective disparity refinement.