Amazon Technologies, Inc.

Deepthi Nandakumar

Avisar Ten-Ami

Charles Benjamin Franklin Waggoner

Lei Li

Sriram Sethuraman

Amarsingh Buckthasingh Winston

Hai Wei

Yang Yang

Vishal Jain

Sandesh Ghanta

Charles Benjamin Waggoner

Sitaraman Ganapathy

Srikanth Kiran Kotagiri

Brian Manuel Alvarez

Srinivas Rajagopalan

Arko Sabui

Daniel Robert Graham

Snehith Alapati

Techniques are described for optimizing streaming video encoding profiles.

Optimizing streaming video encoding profiles

Techniques are described for optimizing and assigning video encoding ladders.

Optimizing and assigning video encoding ladders

Devices and methods are provided for adaptive multi-pass risk-based video encoding. A device may receive a segment of video frames encoded using first encoding parameters. The device may determine a group of pixels in a first video frame of the video frames. The device may determine characteristics associated with the group of pixels and may determine, based on the characteristics and a number of pixels in the group of pixels, a score associated with the segment, wherein the score is indicative of a visibility of banding compression artifact. The device may determine, based on the score, second encoding parameters associated with encoding the segment.

Adaptive multi-pass risk-based video encoding

Techniques for resolution-based manifest generation for adaptive bitrate video streaming are described. As one example, a computer-implemented method includes performing a first, convex hull optimization on a video file for a first device resolution, performing a second, convex hull optimization on the video file for a second, lower device resolution, generating a first video representation for a point on both the first, convex hull optimization and the second, convex hull optimization for the first device resolution and the second, lower device resolution, generating a second video representation for a point only on the second, convex hull optimization for the second, lower device resolution, receiving a request for a manifest for the video file from a client device at the second, lower device resolution, generating the manifest for the client device that identifies the first video representation and the second video representation, and sending the manifest to the client device.

Resolution-based manifest generator for adaptive bitrate video streaming

A video may include a runtime. Based on analysis of the video, a scene or shot change may be generated based on the content of the video. For instance, the video may depict a character inside of a house in a first shot and, in a subsequent shot, the video may depict the character as being outside in a forest. A shot change may indicate the transition from the character being depicted inside in the first shot and outside in the second shot at a period of time during the runtime of the video. The video may be fragmented into fragments based on the shot change and based on a minimum or maximum fragment duration. When a video is fragmented into fragments, a key frame that is independently decodable may be inserted into a fragment. Encoding settings may then be determined based on the key frame.

Optimal fragmentation of video based on shot analysis

Techniques enabled by the present disclosure enable media content to be split into sections that can be encoded by multiple video encoders. The disclosed techniques further enable the separately encoded sections to be stitched back together using coordination logic that does not require bi-directional communication between encoding processes.

Split-and-stitch media content encoding

Techniques for high-quality, segmented video encoding are described. As one example, a computer-implemented method includes receiving a video file, splitting the video file into a plurality of chunks comprising a first chunk of the video file and a second chunk of the video file that is later in video order than the first chunk, encoding the first chunk into a first encoded chunk, determining, separately from the encoding of the first chunk, a rate control state element for a section of the video file immediately before the second chunk in video order, encoding, separately from the encoding of the first chunk, the second chunk into a second encoded chunk using the rate control state element, and assembling the first encoded chunk and the second encoded chunk together to form an encoded video file.

Overlapped rate control for high-quality segmented video encoding

An adaptive video compression system may receive video data to be compressed, such as for delivery to a user device by a video streaming service. For example the video data may be an entire video file or a segment of a video file. The adaptive video compression system determines a suitable encoding scheme for compressing the video data. In order to determine the encoding scheme, the video data may be analyzed to extract a plurality of features interests of the video data, which may represent one or more characteristics of the video data. The features may be concatenated and collectively expressed as a feature vector. The feature vector is then used to determine a classification for the video data. Accordingly, an encoding scheme is determined for the video data based on the classification of the video data, and applied to video data to compress the video data.

Adaptive video compression

Encoding of a video file includes determining a plurality of scenes associated with a video file, and determining at least one group of pictures (GOP). Starting sequentially from a beginning frame of the video file, the system identifies a first GOP having a first encoding error characteristic. The system changes a bitrate allocation setting from a first setting to a second setting based on the encoding error characteristic. The system identifies a second frame having a second encoding error characteristic, and changes a second bitrate allocation setting from the second setting to a third setting based on the second encoding error characteristic. The system generates an encoded video file that includes an encoded plurality of scenes.

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Bengaluru, India

Deepthi Nandakumar