Approaches to selection of motion vector (“MV”) precision during video encoding are presented. These approaches can facilitate compression that is effective in terms of rate-distortion performance and/or computational efficiency. For example, a video encoder determines an MV precision for a unit of video from among multiple MV precisions, which include one or more fractional-sample MV precisions and integer-sample MV precision. The video encoder can identify a set of MV values having a fractional-sample MV precision, then select the MV precision for the unit based at least in part on prevalence of MV values (within the set) having a fractional part of zero. Or, the video encoder can perform rate-distortion analysis, where the rate-distortion analysis is biased towards the integer-sample MV precision. Or, the video encoder can collect information about the video and select the MV precision for the unit based at least in part on the collected information.

Approaches to selection of motion vector (“MV”) precision during video encoding are presented. These approaches can facilitate compression that is effective in terms of rate-distortion performance and/or computational efficiency. For example, a video encoder determines an MV precision for a unit of video from among multiple MV precisions, which include one or more fractional-sample MV precisions and integer-sample MV precision. The video encoder can identify a set of MV values having a fractional-sample MV precision, then select the MV precision for the unit based at least in part on prevalence of MV values (within the set) having a fractional part of zero. Or, the video encoder can perform rate-distortion analysis, where the rate-distortion analysis is biased towards the integer-sample MV precision. Or, the video encoder can collect information about the video and select the MV precision for the unit based at least in part on the collected information.

A method for forming an image sequence that is an output sequence, from an input image sequence, is provided. The input image sequence has an input spatial resolution and an input temporal resolution. The output sequence has an output temporal resolution equal to the input temporal resolution and an output spatial resolution equal to a predetermined fraction 1/N of the input spatial resolution by an integer number N higher than or equal to 2. The method, implemented for a sub-sequence of the input frame sequence that is a current input sub-sequence and including a preset number of images, includes: obtaining a temporal frequency that is an image frequency, associated with the current input sub-sequence; processing the current input sub-sequence to obtain an output sub-sequence; and inserting the output sub-sequence and the associated image frequency into an output container.

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. Following completion of the second pass, the results of both passes are then merged to obtain the necessary information for the encoder to achieve the best possible result.

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. Following completion of the second pass, the results of both passes are then merged to obtain the necessary information for the encoder to achieve the best possible result.

A system can obtain a labelled data set, including historic video data and labelled events. The system can divide the labelled data set into historic training/testing data sets. The system can determine, using the historic training data set, a plurality of different parameter configurations to be used by a video encoder to encode a video that includes a plurality of video frames. Each parameter configuration can include a group of pictures (“GOP”) size and a scenecut threshold. The system can calculate an accuracy of event detection (“ACC”) and a filtering rate (“FR”) for each parameter configuration. The system can calculate, for each parameter configuration of the plurality of different parameter configurations, a harmonic mean between the ACC and the FR. The system can then select a best parameter configuration of the plurality of different parameter configurations based upon the parameter configuration that has the highest harmonic mean.

According to the present invention, an adaptive scheme is applied to an image encoding apparatus that includes an inter-predictor, an intra-predictor, a transformer, a quantizer, an inverse quantizer, and an inverse transformer, wherein input images are classified into two or more different categories, and two or more modules from among the inter-predictor, the intra-predictor, the transformer, the quantizer, and the inverse quantizer are implemented to perform respective operations in different schemes according to the category to which an input image belongs. Thus, the invention has the advantage of efficiently encoding an image without the loss of important information as compared to a conventional image encoding apparatus which adopts a packaged scheme.

Systems and methods are described for obfuscating variants of content segments. Variants of content segments can be used to encode an identifying sequence in a transmission of content. The variants of the content segments can each include one or more marked frames and one or more unmarked frames. Variations can be introduced into the unmarked frames for each of the variants of the content segments.

A video coding system handling at least a block of at least an image of a video comprises an encoding process and decoding process respectively providing or using signaling information related to the video, wherein the signaling information comprises at least an information representative of the presence of chroma offset values, wherein when chroma is present and does not use separate color planes, the information representative of the presence of chroma offset values is set and the signaling information further comprises information representative of a chroma offsets values and wherein when chroma is not present or uses separate color planes, the information representative of the presence of chroma offset values is reset and no information representative of a chroma offsets values is further signaled.

A video packaging and origination service can include one or more encoder components that receive content for encoding and transmitting to requesting entities. During the operation of the encoder components, individual encoders receive input signals for encoding and determine quality metric information related to the generation of an encoded segment. The encoder components exchange quality metric information and an encoder component is selected to transmit an encoded segment. The selection of an individual encoder component per segment can continue throughout the streaming process.