Apple Inc.

Xin Tong

Thomas Pun

Hsi-Jung Wu

Barin Geoffry Haskell

Adriana Dumitras

Jim Normile

Xiaochun Nie

Xiaojin Shi

Peter Chou

Randy Ubillos

Hsi Jung Wu

Courtney Ann Kennedy

Paul Towner

Greg Mullins

Greg Wallace

James O Normile

Daniel Pettigrew

Andrew Bryant

Peter Warner

Zehang Sun

Olivier Fedkiw

Toshihiro Horie

In some implementations, a computing device can track an object from a first image frame to a second image frame using a self-correcting tracking method. The computing device can select points of interest in the first image frame. The computing device can track the selected points of interest from the first image frame to the second image frame using optical flow object tracking. The computing device can prune the matching pairs of points and generate a transform based on the remaining matching pairs to detect the selected object in the second image frame. The computing device can generate a tracking confidence metric based on a projection error for each point of interest tracked from the first frame to the second frame. The computing device can correct tracking errors by reacquiring the object when the tracking confidence metric is below a threshold value.

Long term object tracker

Some embodiments provide a video recording device for capturing a video clip. The video recording device receives a selection of a non-temporally compressed encoding scheme from several different encoding schemes for encoding the video clip. The different encoding schemes include at least one temporally compressed encoding scheme and at least the selected non-temporally compressed encoding scheme. The video recording device captures the video clip as several frames. The video recording device non-temporally encodes each of the frames as several slices. The slices of a particular frame are for decoding by several processing units of a video decoding device. The video recording device stores the video clip in a storage.

Video format for digital video recorder

Techniques for encoding data based at least in part upon an awareness of the decoding complexity of the encoded data and the ability of a target decoder to decode the encoded data are disclosed. In some embodiments, a set of data is encoded based at least in part upon a state of a target decoder to which the encoded set of data is to be provided. In some embodiments, a set of data is encoded based at least in part upon the states of multiple decoders to which the encoded set of data is to be provided.

Complexity-aware encoding

A method of encoding a sequence of video images is described. The method receives the sequence of video images. The method iteratively examines different encoding solutions for the sequence of video images to identify an encoding solution that optimizes image quality while meeting a target bit rate and satisfying a set of constraints regarding flow of encoded data through an input buffer of a hypothetical reference decoder for decoding the encoded video sequence. The iterative examining includes, for each encoding solution, determining whether the hypothetical reference decoder underflows while processing the encoding solution for any set of images within the video sequence.

Multi-pass video encoding solution for buffer underflow

Some embodiments provide a method for detecting and/or identifying a set of faces in a video frame and performing a set of image processing operations based on locations of the set of faces. In particular, the method identifies a set of respective locations of the set of faces in the video frame and applies one or more image processing operations based on the locations of the set of faces found in the video frame. The image processing operations include color correction operations, non-color correction operations, and image processing operations that modify areas inside or outside of the detected and/or identified faces. Additionally, some embodiments provide a graphical user interface for automatically applying image processing operations to an area of a video frame isolated by an ellipse-shaped mask. Furthermore, some embodiments provide a system for automatically applying image processing operations to an area of a video frame isolated by an ellipse-shaped mask.

Multi-image face-based image processing

In some embodiments, a method of determining encoding type and predictive mode(s) selections for a macroblock of a video frame is provided. In some embodiments, a general method 1) selects the encoding type (16&#xd7;16 or 4&#xd7;4) that is initially considered for a macroblock using an encoding type selection algorithm (based on an attribute of the macroblock that is easy to compute), 2) if the 16&#xd7;16 encoding type is selected in step 1, consider the four 16&#xd7;16 prediction modes that may be used on the macroblock using conventional methods or an improved 16&#xd7;16 predictive mode search algorithm based on distortion thresholds, and 3) if the 4&#xd7;4 encoding type is selected in step 1, select the 4&#xd7;4 prediction mode to be used for each of the sixteen 4&#xd7;4 blocks of the macroblock using conventional methods or an improved 4&#xd7;4 predictive mode search algorithm based on the positional relationships between predictive modes.

Selecting encoding types and predictive modes for encoding video data

A novel multi-pass encoding method that encodes several images (e.g., several frames of a video sequence) is described. The method iteratively performs an encoding operation that encodes these images. The encoding operation is based on a nominal quantization parameter, which the method uses to compute quantization parameter for the images. During several different iterations of the encoding operation, the method uses several different nominal quantization parameters. The method stops its iterations when it reaches a terminating criterion (e.g., it identifies an acceptable encoding of the images).

Multi-pass video encoding based on different quantization parameters

This invention is directed to a video bit rate control method for encoding a video sequence based on a decoder buffer condition and a group of picture (GOP) size limitation of the encoded video sequence. The method includes iteratively adjusting a quantization parameter and/or a masking strength parameter and encoding the video sequence at the adjusted parameters until the buffer condition and GOP size limitation are satisfied. The method makes the above adjustments to avoid buffer underflow and GOP oversizing.

Video bit rate control method

An encoder includes an encoder engine, a storage device and a controller to implement an iterative coding process. The encoder engine compresses a selected portion of a data sequence. The storage device stores the compressed portion of the data sequence after each iteration. The controller selects the portion of the data sequence to compress for each iteration. The controller gathers statistics from the compressed portion of the data sequence. The gathered statistics include statistics generated by the selected frames and statistics extrapolated from the selected frames for the non-selected frames. The controller adjusts coding parameters of the encoder engine on each iteration until the gathered statistics meet a specified performance requirement.

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Mountain View, CA, United States of America

Xin Tong