Abstract: A method and apparatus for performing a frequency-dependent joint component secondary transform (FD-JCST). The method includes obtaining a plurality of transform coefficients in a transform coefficient block; determining whether at least one of the plurality of transform coefficients is a low-frequency coefficient; based on determining that the at least one of the plurality of transform coefficients is the low-frequency coefficient, determining whether the low-frequency coefficient is a non-zero value; and based on determining that the low-frequency coefficient is the non-zero value, performing a joint component secondary transform (JCST) on the low-frequency coefficient and signaling a related syntax to indicate that the JCST is performed.

Abstract: A video coding system in which video images of a video bitstream are rescaled prior to encoding, and again at the decoder upon reception. When encoding a given video frame, the video encoder deduces a level of resampling to apply to a reference frame in order to properly predict blocks in the given video frame or the full given video frame, and carries out one or more predictions by first applying a resampling process on the reference frame data at the deduced level. To decode the given video frame of the bitstream, a video decoder compares a size of the given video frame to sizes of a reference frame to determine a level of resampling for the reference frame data, and carries out predictions to generate predicted data by first applying the determined level of resampling to the reference frame data.

Abstract: A method, computer program, and computer system is encoding or decoding video data. Video data may include a syntax element indicating a quantization index, wherein a range of the quantization index is extended by an offset value.

Abstract: A method, computer program, and computer system for video coding is provided. Video data including one or more quantized coefficients is received. One or more index values associated with the quantized coefficients are mapped to one or more step values based on an exponential mapping. The video data is decoded based on the one or more step values.

Abstract: A method, computer program, and computer system is provided for coding video data. Reference samples and magnitudes of transform coefficients corresponding to a current block of video data from an input to a neural network are identified. Sign values associated with the transform coefficients are predicted based on at least the identified reference samples. The video data is encoded/decoded based on the predicted sign values.

Abstract: A method of decoding may be performed by at least one processor, and may comprise: receiving an entropy coded bitstream comprising compressed video data; generating one or more dequantized blocks, determining whether at least one of a height and a width of the one or more dequantized blocks is greater than or equal to a predefined threshold, and responsive to the at least one of the height or the width of the one or more dequantized blocks being greater than or equal to the predefined threshold, transform coding a dequantized block using a tuned line graph transform (LGT) core to perform direct matrix multiplications for each of the horizontal and vertical dimensions of the one or more dequantized blocks.

Abstract: A method, computer program, and computer system is provided for coding video data. Video data is received. One or more transform cores corresponding to a transform associated with the video data are identified. The one or more transform cores include one or more of a line graph transform (LGT) and a discrete cosine transform (DCT) The video data is decoded based on the identified transform core. The transform cores correspond to one or more from among an 8-bit transform core and a 10-bit transform core. The transform corresponds to one or more from among a 2-point transform, a 4-point transform, an 8-point transform, a 16-point transform, a 32-point transform, and a 64-point transform.

Abstract: A method, computer program, and computer system is provided for coding video data. Video data is received. One or more transform cores corresponding to a transform associated with the video data are identified. The one or more transform cores include one or more of a line graph transform (LGT) and a discrete sine transform (DST) The video data is decoded based on the identified transform core. The transform cores correspond to one or more from among an 8-bit transform core and a 10-bit transform core. The transform corresponds to one or more from among a 2-point transform, a 4-point transform, an 8-point transform, and a 16-point transform.

Abstract: A system includes code including obtaining code to obtain a first syntax element that indicates a first quantization index value; at least one second syntax element that indicates an offset value, a second quantization index value for another coefficient by combining the first quantization index value and the offset value to obtain a combined value, and modifying the combined value to be a predetermined minimum value as the second quantization index value, fourth obtaining code to obtain a quantization step size that corresponds to the second quantization index value; and determining code to determine a mode in which the coded image is to be decoded based on determining whether the first quantization index value is equal to a quantization index value associated with lossless coding, and based on determining whether the offset value is less than or equal to the quantization index value associated with the lossless coding.

Abstract: Video quality analysis may be used in many multimedia transmission and communication applications, such as encoder optimization, stream selection, and/or video reconstruction. An objective VQA metric that accurately reflects the quality of processed video relative to a source unprocessed video may take into account both spatial measures and temporal, motion-based measures when evaluating the processed video. Temporal measures may include differential motion metrics indicating a difference between a frame difference of a plurality of frames of the processed video relative to that of a corresponding plurality of frames of the source video. In addition, neural networks and deep learning techniques can be used to develop additional improved VQA metrics that take into account both spatial and temporal aspects of the processed and unprocessed videos.

Abstract: A video coding system in which video images of a video bitstream are rescaled prior to encoding, and again at the decoder upon reception. When encoding a given video frame, the video encoder deduces a level of resampling to apply to a reference frame in order to properly predict blocks in the given video frame or the full given video frame, and carries out one or more predictions by first applying a resampling process on the reference frame data at the deduced level. To decode the given video frame of the bitstream, a video decoder compares a size of the given video frame to sizes of a reference frame to determine a level of resampling for the reference frame data, and carries out predictions to generate predicted data by first applying the determined level of resampling to the reference frame data.

Abstract: Video quality analysis may be used in many multimedia transmission and communication applications, such as encoder optimization, stream selection, and/or video reconstruction. An objective VQA metric that accurately reflects the quality of processed video relative to a source unprocessed video may take into account both spatial measures and temporal, motion-based measures when evaluating the processed video. Temporal measures may include differential motion metrics indicating a difference between a frame difference of a plurality of frames of the processed video relative to that of a corresponding plurality of frames of the source video. In addition, neural networks and deep learning techniques can be used to develop additional improved VQA metrics that take into account both spatial and temporal aspects of the processed and unprocessed videos.

Abstract: Encoding and decoding systems disclosed enhance the AV1/VPX codecs in the context of 8-bit SDR video and 10-bit HDR video content, for applications including streaming and high quality coding for content contribution editing. For SDR content, lapped biorthogonal transforms for near lossless applications and used and optimized resampling filter pairs for adaptive resolution coding in streaming applications are used. For HDR content, a data adaptive grading technique in conjunction with the VP9/VP10 encoder may be used. The encoding/decoding system provides substantial value in the coding of HDR content, and provides backward compatibility with SDR.

Abstract: Artificial intelligence based techniques are used for analysis of 3D objects in conjunction with each other. A 3D model of two or more 3D objects is generated. Features of 3D objects are matched to develop a correspondence between the 3D objects. Two 3D objects are geometrically mapped and an object is overlayed on another 3D object to obtain a superimposed object. Match analysis of 3D objects is performed based on machine learning based models to determine how well the objects are spatially matched. The analysis of the objects is used in augmented reality applications.

Abstract: Video quality analysis may be used in many multimedia transmission and communication applications, such as encoder optimization, stream selection, and/or video reconstruction. An objective VQA metric that accurately reflects the quality of processed video relative to a source unprocessed video may take into account both spatial measures and temporal, motion-based measures when evaluating the processed video. Temporal measures may include differential motion metrics indicating a difference between a frame difference of a plurality of frames of the processed video relative to that of a corresponding plurality of frames of the source video. In addition, neural networks and deep learning techniques can be used to develop additional improved VQA metrics that take into account both spatial and temporal aspects of the processed and unprocessed videos.

Abstract: Video quality analysis may be used in many multimedia transmission and communication applications, such as encoder optimization, stream selection, and/or video reconstruction. An objective VQA metric that accurately reflects the quality of processed video relative to a source unprocessed video may take into account both spatial measures and temporal, motion-based measures when evaluating the processed video. Temporal measures may include differential motion metrics indicating a difference between a frame difference of a plurality of frames of the processed video relative to that of a corresponding plurality of frames of the source video. In addition, neural networks and deep learning techniques can be used to develop additional improved VQA metrics that take into account both spatial and temporal aspects of the processed and unprocessed videos.

Abstract: Embodiments of the invention receive videos and feedback data associated with the videos from a client device and adaptively encode the videos based on the feedback data. The encoded videos are suitable to be transmitted over a network and displayed on the client device. Embodiments of an encoding server adaptively changes resolution of a video on the fly or scale the video quality up or down based on the factors described by the feedback data, including network condition for transmitting the encoded video, network delay, encoder and decoder processing capacity and feedback from viewers of the decoded video. Furthermore, the encoding server adaptively encodes the video based on a combination of various factors described by the feedback data.

Abstract: Embodiments of the invention recognize human visual gestures, as captured by image and video sensors, to develop a visual language for a variety of human computer interfaces. One embodiment of the invention provides a computer-implement method for recognizing a visual gesture portrayed by a part of human body such as a human hand, face or body. The method includes steps of receiving the visual signature captured in a video having multiple video frames, determining a gesture recognition type from multiple gesture recognition types including shaped-based gesture, position-based gesture, motion-assisted and mixed gesture that combining two different gesture types. The method further includes steps of selecting a visual gesture recognition process based on the determined gesture type and applying the selected visual gesture recognition process to the multiple video frames capturing the visual gesture to recognize the visual gesture.

Abstract: Embodiments of the invention recognize human visual gestures, as captured by image and video sensors, to develop a visual language for a variety of human computer interfaces. One embodiment provides a method for recognizing a hand gesture positioned by a user hand. The method includes steps of capturing a digital color image of a user hand against a background, applying a general parametric model to the digital color image of the user hand to generate a specific parametric template of the user hand, receiving a second digital image of the user hand positioned to represent a hand gesture, detecting a hand contour of the hand gesture based at least in part on the specific parametric template of the user hand, and recognizing the hand gesture based at least in part on the detected hand contour. Other embodiments include recognizing hand gestures, facial gestures or body gestures captured in a video.

Abstract: Embodiments of the invention receive videos and feedback data associated with the videos from a client device and adaptively encode the videos based on the feedback data. The encoded videos are suitable to be transmitted over a network and displayed on the client device. Embodiments of an encoding server adaptively changes resolution of a video on the fly or scale the video quality up or down based on the factors described by the feedback data, including network condition for transmitting the encoded video, network delay, encoder and decoder processing capacity and feedback from viewers of the decoded video. Furthermore, the encoding server adaptively encodes the video based on a combination of various factors described by the feedback data.