Abstract: Methods, systems, and apparatuses are provided to perform phase-detection autofocus control. By way of example, the methods can receive luminance values measured by a plurality of sensing elements in a sensor array, and the sensing elements can include imaging pixels and phase-detection pixels. The methods can compare luminance values measured by at least one of the phase-detection pixels to luminance values associated with a subset of the imaging pixels including two or more imaging pixels. The comparison can be performed at extended horizontal density or full horizontal density along a first sensor-array row that includes the at least one phase-detection pixel and the two or more imaging pixels. The methods can also perform a phase-detection autofocus operation based on an outcome of the comparison.

Abstract: Methods, systems, and apparatuses are provided to perform phase-detection autofocus control. By way of example, the methods can receive luminance values measured by a plurality of sensing elements in a sensor array, and the sensing elements can include imaging pixels and phase-detection pixels. The methods can compare luminance values measured by at least one of the phase-detection pixels to luminance values associated with a subset of the imaging pixels including two or more imaging pixels. The comparison can be performed at extended horizontal density or full horizontal density along a first sensor-array row that includes the at least one phase-detection pixel and the two or more imaging pixels. The methods can also perform a phase-detection autofocus operation based on an outcome of the comparison.

Abstract: An imaging system may be provided having an image sensor and a fixed-rate codec for encoding image data from the image sensor into a fixed-rate bitstream. The image sensor may include an array of image pixels with a corresponding Bayer pattern array of color filter elements. The codec may include circuits for partitioning the image data into fixed-size blocks of image data and compressing the image data in each fixed-size block based on the image content in that block using a logarithm-based quantization of selected transform coefficients. The available bits for each block may be allocated to various components such as color components of the data based on the complexity of the image content in each component. The bitstream may include header information with pointers to coefficient locations within each block. The header information may be compressed prior to insertion into the bitstream.

Abstract: Imaging systems with image sensors and image processing circuitry are provided. The image processing circuitry may identify motion and perform autofocus (e.g., continuous autofocus) using images captured by an image sensor. Auto exposure metrics such as average luminance values and autofocus statistics such as sharpness scores may be calculated for each image. The auto exposure metrics may be used to calculate motion scores and identify directional motion between a series of captured images. The motion scores may be used with the sharpness scores to determine when to perform autofocus functions such as when to refocus a lens for a continuous autofocus application. For example, the motion scores may be monitored to identify motion that exceeds a given magnitude and duration. After identification of motion, motion scores and sharpness scores may be used to determine when a given scene has stabilized and when the lens should be refocused.

Abstract: An imaging system may be provided haying an image sensor and a fixed-rate codec for encoding image data from the image sensor into a fixed-rate bitstream. The image sensor may include an array of image pixels with a corresponding Bayer pattern array of color filter elements. The codec may include circuits for partitioning the image data into fixed-size blocks of image data and compressing the image data in each fixed-size block based on the image content in that block using, a logarithm-based quantization of selected transform coefficients. The available bits for each block. may be allocated to various components such as color components of the data based on the complexity of the image content in each component. The bitstream may include header information with pointers to coefficient locations within each block. The header information may be compressed prior to insertion into the bitstream.

Abstract: This is generally directed to auto-focusing techniques based on statistical blur estimation. An image can be captured at two or more candidate lens positions. The amount of blur of each image can then be determined, and the image containing the least amount of blur can be chosen as the “in-focus” image. In some embodiments, the amount of blur of an image can be determined by identifying how “Gaussian” an image is. Characteristics that are more Gaussian in nature can indicate that the image is more blurry. The Gaussianity of an image can be determined by estimating a generalized Gaussian shape parameter for that image. A smaller shape parameter can indicate the image is less Gaussian in nature. The shape parameter can be estimated in any suitable manner such as, for example, through a 2-d discrete wavelet transform, through a 1-d discrete wavelet transform, or through any other suitable manner.

Abstract: Imaging systems with image sensors and image processing circuitry are provided. The image processing circuitry may identify motion and perform autofocus (e.g., continuous autofocus) using images captured by an image sensor. Auto exposure metrics such as average luminance values and autofocus statistics such as sharpness scores may be calculated for each image. The auto exposure metrics may be used to calculate motion scores and identify directional motion between a series of captured images. The motion scores may be used with the sharpness scores to determine when to perform autofocus functions such as when to refocus a lens for a continuous autofocus application. For example, the motion scores may be monitored to identify motion that exceeds a given magnitude and duration. After identification of motion, motion scores and sharpness scores may be used to determine when a given scene has stabilized and when the lens should be refocused.

Abstract: This is generally directed to auto-focusing techniques based on statistical blur estimation. An image can be captured at two or more candidate lens positions. The amount of blur of each image can then be determined, and the image containing the least amount of blur can be chosen as the “in-focus” image. In some embodiments, the amount of blur of an image can be determined by identifying how “Gaussian” an image is. Characteristics that are more Gaussian in nature can indicate that the image is more blurry. The Gaussianity of an image can be determined by estimating a generalized Gaussian shape parameter for that image. A smaller shape parameter can indicate the image is less Gaussian in nature. The shape parameter can be estimated in any suitable manner such as, for example, through a 2-d discrete wavelet transform, through a 1-d discrete wavelet transform, or through any other suitable manner.