Embodiments are described for dynamically adapting video encoding to maintain a nearly stable frame rate based on processor capabilities and bandwidth, for example, by varying a quantization parameter. The quality of the encoded video can be varied to maintain the nearly constant frame rate, which may be measured from the number of encoded video frames being transmitted over a network interface.

An image processing device that updates a pixel value of a processing target image and generates a new image generates a first feature vector based on the processing target image and a first feature map generated with at least one pre-decided filter; updates the processing target image to generate an updated image; generates a second feature vector based on the updated image and a second feature map generated with at least one pre-decided filter; performs quality evaluation of the updated image based on the first and second feature vectors and generates a quality feedback vector which is a vector based on a result of the quality evaluation; performs an encoding amount evaluation on the updated image and generates an encoding amount feedback vector which is a vector based on a result of the encoding amount evaluation; and determines an updating amount in updating of the updated image based on the quality feedback vector and the encoding amount feedback vector.

A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

A method for performing combined inter and intra prediction (CIIP) includes determining the CIIP being enabled for a target block; determining a first intra prediction mode of the target block using a template-based intra mode derivation (TIMD) method; generating an intra predictor of the target block with the first intra prediction mode; and obtaining a final predictor of the target block by weighted averaging the intra predictor of the target block and an inter predictor of the target block.

When a temporally compressed video stream is decoded and subsequently re-encoded, quality is typically lost. The quality loss may be mitigated using information about how the source video stream was encoded during the re-encoding process. According to some aspects of the disclosure, this mitigation of quality loss can be facilitated by decoders that output such information and encoders that receive such information. These decoders and encoders may be separate devices. The functionality of these decoders and encoders may also be combined in a single device, such as a transcoding device. An example of the information that may be used during re-encoding is whether each portion of the original stream was intra-coded or non-intra-coded.

When a temporally compressed video stream is decoded and subsequently re-encoded, quality is typically lost. The quality loss may be mitigated using information about how the source video stream was encoded during the re-encoding process. According to some aspects of the disclosure, this mitigation of quality loss can be facilitated by decoders that output such information and encoders that receive such information. These decoders and encoders may be separate devices. The functionality of these decoders and encoders may also be combined in a single device, such as a transcoding device. An example of the information that may be used during re-encoding is whether each portion of the original stream was intra-coded or non-intra-coded.

A set of tensor-product B-Spline (TPB) basis functions is determined. A set of selected TPB prediction parameters to be used with the set of TPB basis functions for generating predicted image data in mapped images from source image data in source images of a source color grade is generated. The set of selected TPB prediction parameters is generated by minimizing differences between the predicted image data in the mapped images and reference image data in reference images of a reference color grade. The reference images correspond to the source images and depict same visual content as depicted by the source images. The set of selected TPB prediction parameters is encoded in a video signal as a part of image metadata along with the source image data in the source images. The mapped images are caused to be reconstructed and rendered with a recipient device of the video signal.

A set of tensor-product B-Spline (TPB) basis functions is determined. A set of selected TPB prediction parameters to be used with the set of TPB basis functions for generating predicted image data in mapped images from source image data in source images of a source color grade is generated. The set of selected TPB prediction parameters is generated by minimizing differences between the predicted image data in the mapped images and reference image data in reference images of a reference color grade. The reference images correspond to the source images and depict same visual content as depicted by the source images. The set of selected TPB prediction parameters is encoded in a video signal as a part of image metadata along with the source image data in the source images. The mapped images are caused to be reconstructed and rendered with a recipient device of the video signal.

Examples described herein relate to decoding and encoding signals. Certain examples described herein encapsulate custom data that is not signal data within a stream of encoded signal data. The custom data may comprise a wide variety of metadata that annotates the signal data, or provides additional information relating to the signal data. Certain examples described herein encapsulate custom data within a set of transformed coefficient values that represent data derived from a transform operation that forms part of the signal encoding. The encapsulation is may be performed by applying a bit shift operation to coefficient bits representing the set of transformed coefficient values.

Examples described herein relate to decoding and encoding signals. Certain examples described herein encapsulate custom data that is not signal data within a stream of encoded signal data. The custom data may comprise a wide variety of metadata that annotates the signal data, or provides additional information relating to the signal data. Certain examples described herein encapsulate custom data within a set of transformed coefficient values that represent data derived from a transform operation that forms part of the signal encoding. The encapsulation is may be performed by applying a bit shift operation to coefficient bits representing the set of transformed coefficient values.