Abstract: An original input content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.

Abstract: An original input content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate: table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.

Abstract: An original input content is subjected to multiple constant quality probe encodes for a defined set of resolutions. In one embodiment, probe encodes encode a few parts of the original source video, for example, 30 seconds from 5 different positions. Each probe encode delivers an average bitrate that is required to achieve the configured constant quality. The mean value of the average bitrate is taken per resolution. This results in a list of bitrates that map to a resolution that would achieve the best quality, a custom bitrate table. Based on the custom bitrate table, an optimized bitrate ladder is computed. The process starts with a configurable minimum bitrate and steps up by a bitrate step size that is between a configurable min and max bitrate step size until the bitrate of the highest resolution is reached.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.

Abstract: A video streaming system optimizes the buffering of periods of frames of a video presentation in order to achieve a more constant perceptual quality throughout the entire video presentation. An adaption algorithm determines transmission bitrates to transmit some periods at a lower bitrate that the channel conditions of the channel may allow while transmitting other periods at a higher bitrate. The transmission bitrates are determined based on expected quality metadata signaled in the periods of the bitstream for the current period and following periods in order to optimize the bitrate and the expected perceptual quality of each version of each period over time.

Abstract: An original input video file is encoded using a machine learning approach. The encoder performs a detailed video analysis and selection of encoding parameters that using a machine learning algorithm improves over time. The encoding process is done using a multi-pass approach. During a first pass, the entire video file is scanned to extract video property information that does not require in-depth analyses. The extracted data is then entered into an encoding engine, which uses artificial intelligence to produce optimized encoder settings. The video file is into a set of time-based chunks and, in a second pass, the encoding parameters for each chunk are set and distributed to encoding nodes for parallel processing. These encoder instances probe-encode each chunk determine the level of complexity for the chunk and to derive chunk-specific encoding parameters.