Apple Inc.

Jeffrey E Frederiksen

Guy Cote

Guy Côté

Joseph P Bratt

Michael G Frank

Timothy John Millet

Ian C Hendry

Paul M Hubel

Sumit Chawla

Jung Wook Cho

Shun Wai Go

Albert C Kuo

Robert L Ridenour

Guy CÔTÉ

Techniques are provided for determining an optimal focal position using auto-focus statistics. In one embodiment, such techniques may include generating coarse and fine auto-focus scores for determining an optimal focal length at which to position a lens associated with the image sensor. For instance, the statistics logic may determine a coarse position that indicates an optimal focus area which, in one embodiment, may be determined by searching for the first coarse position in which a coarse auto-focus score decreases with respect to a coarse auto-focus score at a previous position. Using this position as a starting point for fine score searching, the optimal focal position may be determined by searching for a peak in fine auto-focus scores. In another embodiment, auto-focus statistics may also be determined based on each color of the Bayer RGB, such that, even in the presence of chromatic aberrations, relative auto-focus scores for each color may be used to determine the direction of focus.

Auto-focus control using image statistics data with coarse and fine auto-focus scores

Lossless image compression using differential transfers may involve an image compression unit receiving image data for an image in a sequence of images and transmitting the image data such that image data for at least some image tiles is transmitted using lossy compression due to resource limitations. The image compression unit may then receive image data for a subsequent image in the sequence and determine that the image data for at least some tiles does not change relative to the image data for corresponding tiles of the previous image. The image compression unit may then transmit image data in a manner sufficient to create lossless versions of tiles for which lossily compressed image data was sent previously.

Lossless image compression using differential transfer

Certain aspects of this disclosure relate to an image signal processing system that includes a flash controller that is configured to activate a flash device prior to the start of a target image frame by using a sensor timing signal. In one embodiment, the flash controller receives a delayed sensor timing signal and determines a flash activation start time by using the delayed sensor timing signal to identify a time corresponding to the end of the previous frame, increasing that time by a vertical blanking time, and then subtracting a first offset to compensate for delay between the sensor timing signal and the delayed sensor timing signal. Then, the flash controller subtracts a second offset to determine the flash activation time, thus ensuring that the flash is activated prior to receiving the first pixel of the target frame.

Flash synchronization using image sensor interface timing signal

In an embodiment, a compression unit is provided which may perform compression of a pixel stream. Similarly, a decompression unit may be provided which may decompress the compressed pixel stream. The compression and decompression units may include a streaming wavelet transform, which may perform the wavelet transform through a pipeline of wavelet operations. Each section of the pipeline may perform a pass of the wavelet transform on the pixel stream, and the section may provide input to another section of the pipeline that performs a next pass of the wavelet transform. The transform may thus be performed on the fly as the pixels are received.

Streaming wavelet transform

Various techniques are disclosed for collecting and processing auto-focus statistics data in an image signal processor (ISP). In one embodiment, a statistics collection engine in an ISP front-end processing unit may be configured to collect coarse (based on decimated raw data) and fine auto-focus statistics. Coarse auto-focus statistics may be collected on decimated Bayer RGB data and/or on linear camera luma values. Fine auto-focus statistics may be collected on raw Bayer RGB using a combination of a horizontal filter and edge detector, or may be collected on BayerY data (by applying a 3&#xd7;1 transform to the raw Bayer RGB data). Edge sums may be accumulated using the filter outputs to determine auto-focus statistics.

Techniques for collection of auto-focus statistics

In an embodiment, a compression unit is provided which may perform compression of images with low latency and relatively little hardware. Similarly, a decompression unit may be provided which may decompress the images with low latency and hardware. In an embodiment, the transmission of compressed coefficients may be performed using less than two passes through the list of coefficients. During the first pass, the most significant coefficients may be transmitted and other significance groups may be identified as linked lists. The linked lists may then be traverse to send the other significance groups. In an embodiment, a color space conversion may be made to permit filtering of fewer color components than might be possible in the source color space.

Memory compression technique with low latency per pixel

Disclosed embodiments provide for a an image signal processing system that includes back-end pixel processing unit that receives pixel data after being processed by at least one of a front-end pixel processing unit and a pixel processing pipeline. In certain embodiments, the back-end processing unit receives luma/chroma image data and may be configured to apply face detection operations, local tone mapping, bright, contrast, color adjustments, as well as scaling. Further, the back-end processing unit may also include a back-end statistics unit that may collect frequency statistics. The frequency statistics may be provided to an encoder and may be used to determine quantization parameters that are to be applied to an image frame.

System and method for processing image data using an image signal processor having back-end processing logic

The present disclosure provides techniques for performing audio-video synchronization using an image signal processing system. In one embodiment, a time code register provides a current time stamp when sampled. The value of the time code register may be incremented at regular intervals based on a clock of the image signal processing system. At the start of a current frame acquired by an image sensor, the time code register is sampled, and a timestamp is stored into a timestamp register associated with the image sensor. The timestamp is then read from the time stamp register and written to a set of metadata associated with the current frame. The timestamp stored in the frame metadata may then be used to synchronize the current frame with a corresponding set of audio data.

Techniques for synchronizing audio and video data in an image signal processing system

Various techniques are provided herein for the demosaicing of images acquired and processed by an imaging system. The imaging system includes an image signal processor and image sensors utilizing color filter arrays (CFA) for acquiring red, green, and blue color data using one pixel array. In one embodiment, the CFA may include a Bayer pattern. During image signal processing, demosaicing may be applied to interpolate missing color samples from the raw image pattern. In one embodiment, interpolation for the green color channel may include employing edge-adaptive filters with weighted gradients of horizontal and vertical filtered values. The red and blue color channels may be interpolated using color difference samples with co-located interpolated values of the green color channel. In another embodiment, interpolation of the red and blue color channels may be performed using color ratios (e.g., versus color difference data).

System and method for demosaicing image data using weighted gradients

Certain embodiments disclosed herein relate to an image signal processing system includes overflow control logic that detects an overflow condition when a destination unit when a sensor input queue and/or front-end processing unit receives back pressure from a downstream destination unit. In one embodiment, pixels of a current frame are dropped when an overflow condition occurs. The number of dropped pixels may be tracked using a counter. Upon recovery of the overflow condition, the remaining pixels of the frame are received and each dropped pixel may be replaced using a replacement pixel value.

Overflow control techniques for image signal processing

Various techniques are disclosed for processing statistics data in an image signal processor (ISP). In one embodiment, a statistics collection engine may be configured to acquire statistics relating to auto white-balance. The statistics collection engine may receive raw Bayer RGB data acquired by an image sensor and may be configured to perform one or more color space conversions to obtain pixel data in other color spaces. A set of pixel filters may be configured to accumulate sums of the pixel data conditionally based upon YC1C2 characteristics, as defined by a pixel condition per pixel filter. Depending on a selected color space, the pixel filters may generate color sums, which may be used to match a current illuminant against a set of reference illuminants with which the image sensor has been previously calibrated.

Flexible color space selection for auto-white balance processing

Temporally filtering raw image data may include a temporal filter that determines a spatial location of a current pixel and identifies at least one collocated reference pixel from a previous frame. A motion delta value is determined based upon the current pixel and its collocated reference pixel. An index is determined based upon the motion delta value and a motion history value corresponding to the current pixel from the previous frame. Using the index, a first filtering coefficient may be selected from a motion table. Then an attenuation factor may be selected from a luma table based upon the value of the current pixel, and a second filtering coefficient may be determined based upon the selected attenuation factor and the first filtering coefficient. The temporally filtered output value corresponding to the current pixel may then be based upon the second filtering coefficient, the current pixel, and the collocated reference pixel.

Temporal filtering techniques for image signal processing

Various techniques are disclosed for processing statistics data in an image signal processor (ISP). In one embodiment, a statistics collection engine may be implemented in a front-end processing unit of the ISP, such that statistics are collected prior to processing by an ISP pipeline downstream from the front-end processing unit. In one embodiment, the statistics collection engine may be configured to acquire statistics relating to auto white-balance, auto-exposure, and auto-focus, as well as flicker detection. Collected statistics may be output to a memory and used by the ISP to process acquired image data.

Techniques for acquiring and processing statistics data in an image signal processor

Various techniques for applying binning compensation filtering to binned raw image data acquired by an image sensor are provided. In one embodiment, a binning compensation filter (BCF) includes separate digital differential analyzers (DDA) for vertical and horizontal scaling. A current position of an output pixel is determined by incrementing the DDA based upon a step size. Using the known output pixel position, a center source input pixel and an index corresponding to the between-pixel fractional position of the output pixel position relative to the input pixels may be selected for filtering. Using the selected center input pixel, one or more same-colored neighboring source pixels may be selected. The number of selected source pixels may depend on the number of taps used by the scaling logic, and may depend on whether horizontal or vertical scaling is being applied. Using the selected index, a set of filter coefficients may be selected from a filter coefficient lookup table, applied to the selected source pixels, and the results may be summed to determine a value for an output pixel having a position corresponding to the current position of the DDA. This process may be repeated for each input pixel and may be performed in both vertical and horizontal directions, thus ultimately producing a re-sampled set of image data that is spatially evenly distributed.

Growing community of inventors

Sunnyvale, CA, United States of America

Jeffrey E Frederiksen