Arcsoft Corporation Limited

Lingxiang Zhou

Donghui Wu

Yushan Huang

Yiqing Jin

Kaixuan Mao

Anding Zhu

Wei Xiong

Min Cheng

Xingguo Wang

Guobin Chen

Yunfang Zhu

Huiqiong Wang

A method is provided for processing minimum coded units (MCUs) in a Joint Photographic Expert Group (JPEG) bit stream representing an image. A data structure is generated for storing MCU block numbers, corresponding MCU block coordinates, and corresponding MCU block editing lists. When an editing operation to the image is received, the editing operation is saved to a group of the MCU block editing lists of a group of the MCUs affected by the editing operation. Later the MCUs are retrieved from the JPEG bit stream one at a time and then processed according to the editing operations in the corresponding MCU blocking editing lists.

JPEG processing engine for low profile systems

A method for enhancing an image includes applying an auto-level transformation to an original image to form a first image, applying an auto-contrast transformation to the original image to form a second image, and applying an auto-brightness transformation to the original image to form a third image. The method further includes applying a color cast correction to the first, the second, and the third images to generate a first group of images, applying a gamma correction to the first group of images to generate a second group of images, applying a sharpening correction to the second group of images to generate a third group of images, and presenting the third group of images to a user to select a final result.

Better picture for inexpensive cameras

A method for searching an image database includes capturing an image of a photograph and a background, determining a boundary of the photograph in the image, cropping the photograph from the image, correcting the perspective of the photograph, compensating colors of the photograph, and matching the photograph with an image in the image database.

Image retrieval

A method for correcting a radial distortion in an image includes receiving a radial distortion parameter from the user and calculating new intensity values of points in the image to correct a radial exposure distortion in the image.

A method for real-time video encoding includes buffering frames in an encoder input buffer, retrieving the frames from the encoder input buffer, encoding the retrieved frames into a bit stream, monitoring the encoder input buffer for buffer overflow, and, if the encoder input buffer is about to overflow, encoding one of the retrieved frames as a pseudo-frame that completely references one reference frame of the one retrieved frame. Encoding one of the retrieved frames as a pseudo-frame bypasses a majority of the encoding procedures to improve the overall encoding speed. The method further includes monitoring a hypothetical decoder input buffer that models an actual decoder input buffer in a video decoder, encoding one of the retrieved frames with a pseudo-frame if the hypothetical decoder input buffer is about to underflow, and adding stuffing bits to the bit stream if the hypothetical decoder input buffer is about to overflow.

Pseudo-frames for MPEG-2 encoding

A method for generating a panoramic image includes receiving a first image, dividing the first image into a first portion and a second portion, rotating the first portion of the first image, saving the rotated first portion of the first image in a nonvolatile memory, receiving a second image, dividing the second image into a third portion and a fourth portion, matching an overlapping region between the second portion of the first image and the third portion of the second image, stitching the second portion of the first image and the third portion of the second image to form a first stitched image, rotating the first stitched image, and saving the first stitched image in the nonvolatile memory.

Panoramic maker engine for a low profile system

A method for creating a 360 degree panoramic image from multiple images includes (1) computing a gross rotation error &#x394;R between a first image and a calculated first image rotated to be stitched to a last image, and (2) spreading the gross rotation error &#x394;R to each pixel on the panoramic image. Spreading the gross rotation error &#x394;R includes (1) computing a rotation angle &#x3b8;and rotational axis nfrom the gross rotational error &#x394;R, (2) determining an angle &#x3b1; of each pixel, and (3) determining a compensation matrix Rfor each pixel using the following formula: R(&#x3b1;)=R(&#x3b1;/2&#x3c0;&#x3b8;). Spreading the gross rotation error &#x394;R further includes (4) tracing a pixel on the panoramic image to a camera optical center of the images to form a first ray, (5) determining a second ray originating from the camera optical center that would be rotated by the compensation matrix Rto coincide with the first ray, (6) tracing the second ray to a second pixel on one of the images, and (7) painting the first pixel with color values of the second pixel.

Image warping correction in forming 360 degree panoramic images

A method for indexing minimum coded units (MCUs) in a Joint Photographic Expert Group (JPEG) bit stream includes (1) entropy decoding a first MCU to determine a bit offset of a second MCU and a DC coefficient of the first MCU, wherein the first MCU immediately precedes the second MCU in the bit stream, and (2) indexing the second MCU by storing the bit offset and the DC coefficient in an index. The method may further include (3) receiving a request for the second MCU, (4) reading the index to determine the bit offset of the second MCU and the DC coefficient of the first MCU, and (5) entropy decoding the second MCU starting at its bit offset in the bit stream, wherein the entropy decoding the second MCU includes determining a DC coefficient of the second MCU using the DC coefficient of the first MCU.

A method for improving image resolution includes (a) selecting a low-resolution pixel in a low-resolution image; (b) generating a vector that characterizes a low-resolution patch about the low-resolution pixel; (c) classifying the low-resolution patch into one of a plurality of possible patterns; (d) if said one pattern is one of a plurality of top ranking patterns having filters, applying a filter of said one of the top ranking patterns to the low-resolution patch to generate high-resolution pixels that correspond to the low-resolution pixel; (e) if said one pattern is not one of the plurality of top ranking patterns, applying an interpolation process to the low-resolution patch to generate the high-resolution pixels; (f) repeating steps (a) to (e) for additional low-resolution pixels to generate additional high-resolution pixels to form a high-resolution image.

Pattern classification and filter design for increasing image resolution

A method for correcting a nonlinear distortion in an image includes receiving an image of a calibration template comprising calibration patterns, detecting at least three points from the calibration patterns, calculating a distortion parameter from coordinates of the at least three points that should align along a straight line, and correcting the nonlinear distortion in the image using the distortion parameter. A method for correcting a radial distortion in an image includes receiving a radial distortion parameter from the user and calculating new intensity values of points in the image to correct a radial exposure distortion in the image.

A method for generating texture includes (1) selecting a target patch to be filled in a image, (2) selecting a sample patch as a candidate for filling the target patch, (3) determining a first difference between a first area surrounding the target patch and a corresponding first area surrounding the sample patch, and a second difference between a second area surrounding the target patch and a corresponding second area surrounding the sample patch, (4) multiplying a larger of the first difference and the second difference with a first weight factor, and a smaller of the first difference and the second difference with a second weight factor, and (5) summing the weighted first difference and the weighted second difference as a distance between the target patch and the sample patch.

Texture synthesis for repairing damaged images

A method for detecting an image area in a digital image includes identifying in the digital image a first image region indicative of a background area and a second image region indicative of the image area, computing gradient values using the pixel values of the digital image, defining a list of strokes based on the gradient values, merging the list of strokes, defining a list of corners using the list of strokes, and defining an image area rectangle delimiting the image area using the list of corners and the list of strokes. The image area rectangle can be used to define a binding box for extracting the image area from the digital image. The method enables the automatic detection of multiple image areas in a digital image. Moreover, the method implements a robust algorithm for image area detection such that even imperfect image areas can be detected without errors.

Multiple image area detection in a digital image

An image matching method for matching a first image and an overlapping second image includes generating a first set of working layers of the first image and a second set of working layers of the second image. The method determines an overlap between an initial working layer of the first image and an initial working layer of the second image where the initial working layers have a smaller pixel array size and a lower image resolution than the other working layers. The method selects a feature point in the working layer of the first image and determines a position in the working layer of the second image corresponding to the feature point. The method then determines the motion parameters based on the feature point and the position in the first and second images. Finally, the method repeats the selection of a feature point using another working layer of the first image and another working layer of the second image, each of these working layers has a larger pixel array size and a higher image resolution than the initial working layers.

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Fremont, CA, United States of America

Lingxiang Zhou