Techniques are described for determining quality of experience (QoE) rate information for streaming video. For example, QoE rates can be calculated by a client while receiving and decoding an encoded video stream. The QoE rates can be calculated based on the number of video stalls that occur at the client while decoding the encoded video stream during a plurality of time periods. Determining whether a video stall occurs during a given time period involves comparing an encoded frame rate to a decoded frame rate. Indications of the QoE rates can be output.

Systems, apparatuses, and methods are described for detecting malfunctions of computing devices causing outputs of video content. Partially or fully static content frames may be detected, and, indications may be generated based on the static content frames. The indications may be analyzed to identify computing devices with degraded performance. Diagnostic tests may be run by the identified computing devices and/or other actions performed based on a determination of static content frames.

A system and methods are disclosed for optimal format selection for video players based on visual quality. The method includes generating a plurality of reference transcoded versions of a reference video, obtaining quality scores for frames of the plurality of reference transcoded versions of the reference video, generating a first training input comprising a set of color attributes, spatial attributes, and temporal attributes of the frames of the reference video, and generating a first target output for the first training input, wherein the first target output comprises the quality scores for the frames of the plurality of reference transcoded versions of the reference video. The method further includes providing the training data to train a machine learning model on (i) a set of training inputs comprising the first training input and (ii) a set of target outputs comprising the first target output.

An estimation method suitable for a receiver and includes the following steps: calculating a relative signal-to-noise ratio, and determining whether the relative signal-to-noise ratio is higher than, lower than or within a threshold range; in response to determining that the relative signal-to-noise ratio is higher than the threshold range, estimating the signal quality indicator as a first preset value, wherein the first preset value represents a best signal quality; in response to determining that the relative signal-to-noise ratio is higher than the threshold range, estimating the signal quality indicator as a first preset value, wherein the first preset value represents a best signal quality; in response to determining that the relative signal-to-noise ratio is within the threshold range, estimating the signal quality indicator as an output value of a function according to a bit error rate, wherein an input value of the function is the relative signal-to-noise ratio.

Provided is an apparatus comprising: an image acquisition unit configured to acquire a captured image; a compression unit configured to compress a captured image to generate compressed data; a reproduction unit configured to generate, from the compressed data, a reproduced image that reproduces the captured image; an evaluation acquisition unit configured to acquire an evaluation corresponding to a degree of approximation between the reproduced image and the captured image; and a learning processing unit configured to perform learning processing of a model configured to output, in response to an input of a new captured image, a compression parameter value to be applied in compression of the captured image, by using learning data including the evaluation, a captured image corresponding to the evaluation, and a compression parameter value applied in compression of the captured image

A bidirectional media communication channel testing platform includes an HDMI testing device including a video input port and an audio output port; a plurality of media streaming devices, each including a video transmission channel and an audio return channel; and a bidirectional switch including a video path and an audio path. The video path is configured to selectively couple the video input port of the HDMI testing device to a video transmission channel of a selected one of the plurality of media streaming devices, and the audio path is configured to concurrently couple the audio output port of the HDMI testing device to the audio return channel of each of the plurality of media streaming devices, regardless of a switching state of the video path of the bidirectional switch.

In various embodiments, a perceptual quality application computes an absolute quality score for encoded video content. In operation, the perceptual quality application selects a model based on the spatial resolution of the video content from which the encoded video content is derived. The model associates a set of objective values for a set of objective quality metrics with an absolute quality score. The perceptual quality application determines a set of target objective values for the objective quality metrics based on the encoded video content. Subsequently, the perceptual quality application computes the absolute quality score for the encoded video content based on the selected model and the set of target objective values. Because the absolute quality score is independent of the quality of the video content, the absolute quality score accurately reflects the perceived quality of a wide range of encoded video content when decoded and viewed.

A video quality estimation device for estimating quality to be experienced by a user during viewing of a video includes a video quality estimation unit that calculates a video quality estimation value based on a parameter related to video quality of a high image quality region in the video, a parameter related to video quality of a low image quality region in the video, and a parameter related to a time for switching from a low image quality display state to a high image quality display state.

A temporal alignment system and method for example for detecting temporal misalignment in video frames when the frames are divided for transport using a signal divider for dividing a single signal S into portions S.sub.1 . . . S.sub.N and using average picture level in determining whether data sets within a particular frame are misaligned.

Aspects of the disclosure provide for a method. In some examples, the method includes receiving a video stream comprising multiple frames, analyzing the video stream to compare data values representing an image pixel at a specified location in a first of the frames to programmed data values for the image pixel at the specified location in the first of the frames, determining that the video stream includes incorrect data responsive to the data values representing the image pixel at the specified location in the first of the frames being different from the programmed data values for the image pixel at the specified location in the first of the frames; and taking action responsive to determining that the video stream includes incorrect data.