Sony Corporation

Sony Electronics Inc

Kensuke Miyagi

Pingshan Li

Florian Ciurea

Tomonori Shuda

Nobuyuki Matsushita

Earl Quong Wong

Gazi Ali

Hideki Nabesako

Mamoru Sugiura

Masato Watanabe

A methodology for performing a depth estimation procedure with defocused images under extreme lighting conditions includes a camera device with a sensor for capturing blur images of a photographic target under extreme lighting conditions. The extreme lighting conditions may include over-exposed conditions and/or under-exposed conditions. The camera device also includes a depth generator that performs the depth estimation procedure by utilizing the captured blur images. The depth estimation procedure includes a clipped-pixel substitution procedure to compensate for the extreme lighting conditions.

Methodology for performing depth estimation with defocused images under extreme lighting conditions

A system for performing an enhanced scene detection procedure including a sensor device for capturing blur images of a photographic target. The blur images each correspond to a scene type that is detected from a first scene type which is typically a pillbox blur scene, and a second scene type which is typically a Gaussian scene type. A scene detector performs an initial scene detection procedure to identify a candidate scene type for the blur images. The scene detector then performs the enhanced scene detection procedure to identify a final scene type for the blur images.

System and method for utilizing enhanced scene detection in a depth estimation procedure

A system and method for generating a direction confidence measure includes a camera sensor device that captures blur images of a photographic target. A depth estimator calculates matching errors for the blur images. The depth estimator then generates the direction confidence measure by utilizing the matching errors and a dynamic optimization constant that is selected depending upon image characteristics of the blur images.

System and method for evaluating focus direction under various lighting conditions

A system and method for performing a depth estimation procedure utilizing defocused pillbox images includes a camera device with a sensor device for capturing pillbox blur images of a photographic target. The camera utilizes a depth estimator for performing a Gaussianization procedure that transforms the pillbox blur images into corresponding Gaussian blur images. The Gaussianization procedure is performed by convolving the pillbox blur images with a Gaussianization kernel to generate the corresponding Gaussian blur images. The depth estimator then utilizes the Gaussian blur images for effectively performing the depth estimation procedure.

System and method for performing depth estimation utilizing defocused pillbox images

A system and method for performing a depth estimation procedure includes a camera device with a sensor device for capturing blur images of a photographic target. Each of the captured blur images has a corresponding scene type that includes either a Gaussian scene type or a pillbox scene type. A depth generator performs a scene recognition procedure to identify the appropriate scene type for the respective blur images. The depth generator then selects an effective depth estimation procedure depending upon the detected scene type.

System and method for utilizing scene detection in a depth estimation procedure

A method and apparatus for generating a dense depth map. In one embodiment, the method includes applying a joint bilateral filter to a first depth map to generate a second depth map, where at least one filter weight of the joint bilateral filter is adapted based upon content of an image represented by the first depth map, and the second depth map has a higher resolution than the first depth map.

Method and apparatus for generating a dense depth map using an adaptive joint bilateral filter

There are many applications that conduct both generation of contrast or complexity level and motion estimation for video processing. The applications often use a block matching technique. An embedded system such as a personal digital camera is an example of such an application. Additionally, comparison of error differences around the location of minimum error in a motion estimation error table is able to be used to determine low contrast in a scene.

Detection of low contrast for image processing

An apparatus and method for determining confidence in focus direction detection, including the steps of capturing a plurality of images, calculating sets of matching errors and blur difference estimations relating to the images, capturing a plurality of object images; calculating sets of matching errors and blur difference estimations relating to the images, calculating a confidence indicator as a function of either the matching errors or blur difference estimations, and automatically adjusting a focus control element in response to said confidence indicator exceeding a threshold value.

Focus direction detection confidence system and method

Method and apparatus for variable length code (VLC) encoding is described. In some examples, a symbol of frequency transform values having a run and a level is VLC encoded. An address is generated, at a processor, for a lookup table (LUT) in a memory based on the run and the level, the LUT storing VLC entries for all possible combinations of run values ranging from minimum to maximum runs and level values ranging from minimum to maximum levels, each of the VLC entries including a flag indicative of an escape mode. A VLC entry is read from the LUT using the address. A VLC code and bit length are obtained from the VLC entry if the flag in the VLC entry is a first value. A fixed length VLC code is generated from an escape code, the run, and the level if the flag in the VLC entry is a second value.

Method and apparatus for VLC encoding in a video encoding system

A fast iterative motion estimation method enables motion estimation to take place with fewer computations. The motion estimation and error determination steps are combined, each position in the search area is monitored to determine whether error value reaches the minimum over iterations by keeping track of convergence status, a refinement search is applied (thus, a smaller search area than a conventional process), and the difference of the error value and the minimum error value is used for each position in the search area to decide whether to further calculate the error value for that position. Each of these modifications helps minimize the number of computations used in motion estimation.

Fast iterative motion estimation method on gradually changing images

Camera depth estimation is performed in response to picture matching based on blur difference computed between images captured at different focal positions. A blur difference model is stored in the camera based on characterization of the camera with a series of matching curves in which blur difference varies depending on the focal length, aperture, subject distance, and lens focus position. A four-dimensional polynomial model is created to fit the matching curves for use in estimating subject distance. During operation, images are captured for use in estimating subject distance. Motion compensation is applied and blur difference is determined. Blur difference is utilized in the polynomial model to estimate subject distance. Subject distance estimates can be output or utilized within an auto focus process to provide accurate focus adjustments.

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Sunnyvale, CA, United States of America

Kensuke Miyagi