A video camera system including: one or more video cameras; a video recorder in communication with each of the one or more video cameras; a video analytics module, the video analytics module being a computer program product embodied on a computer readable medium, the computer program product including instructions that, when executed by a processor, cause the processor to perform operations including: obtaining video parameters of a plurality of video frames received at the video recorder, the plurality of video frames being transmitted from the one or more video cameras to the video recorder; determining an abnormality within the video parameters; and identifying a malfunctioning video camera of the one or more video cameras that produced the abnormality within the video parameters.

Methods, systems and devices described herein implement a task-driven machine learning-based compression scheme for point cloud geometry implicit representation. The machine learning-based codec is able to be optimized for a task to achieve better compression rates by being conditioned to what the reconstructed signal will be used for. The latent representation of the point cloud or the neural network that implicitly represents the point cloud itself are able to be compressed. The methods described herein perform efficient compression of the implicit representation of a point cloud given a target task.

Methods, systems and devices described herein implement a task-driven machine learning-based compression scheme for point cloud geometry implicit representation. The machine learning-based codec is able to be optimized for a task to achieve better compression rates by being conditioned to what the reconstructed signal will be used for. The latent representation of the point cloud or the neural network that implicitly represents the point cloud itself are able to be compressed. The methods described herein perform efficient compression of the implicit representation of a point cloud given a target task.

A signal-processing apparatus includes an instruction-parallel processor, a first data-parallel processor, a second data-parallel processor, and a motion detection unit, a de-blocking filtering unit and a variable-length coding/decoding unit which are dedicated hardware. With this structure, during signal processing of an image compression and decompression algorithm needing a large amount of processing, the load is distributed between software and hardware, so that the signal-processing apparatus can realize high processing capability and flexibility.

A signal-processing apparatus includes an instruction-parallel processor, a first data-parallel processor, a second data-parallel processor, and a motion detection unit, a de-blocking filtering unit and a variable-length coding/decoding unit which are dedicated hardware. With this structure, during signal processing of an image compression and decompression algorithm needing a large amount of processing, the load is distributed between software and hardware, so that the signal-processing apparatus can realize high processing capability and flexibility.

A neural-network-based signal processing method and apparatus according to the present invention may: receive a bitstream including information about a neural network model, wherein the bitstream includes at least one neural network access unit; obtain information about the at least one neural network access unit from the bitstream; and reconstruct the neural network model on the basis of the information about the at least one neural network access unit.

A neural-network-based signal processing method and apparatus according to the present invention may: receive a bitstream including information about a neural network model, wherein the bitstream includes at least one neural network access unit; obtain information about the at least one neural network access unit from the bitstream; and reconstruct the neural network model on the basis of the information about the at least one neural network access unit.

A casting control method and apparatus are provided. The casting control method and apparatus are applied to the field of video processing technologies. The method is applied to a first electronic device. The first electronic device includes a GPU and an HWC. When the first electronic device is in a casting state, the to-be-synthesized layer can be separately sent to the GPU and the HWC if the video security layer is not included. The first electronic device synthesizes the to-be-synthesized layer in parallel by using the GPU and the HWC.

A casting control method and apparatus are provided. The casting control method and apparatus are applied to the field of video processing technologies. The method is applied to a first electronic device. The first electronic device includes a GPU and an HWC. When the first electronic device is in a casting state, the to-be-synthesized layer can be separately sent to the GPU and the HWC if the video security layer is not included. The first electronic device synthesizes the to-be-synthesized layer in parallel by using the GPU and the HWC.

Provided are a device and operating method thereof for obtaining compression ratio information for recognizing a target object in an image using a deep neural network model, and compressing an image using the compression ratio information and encoding the compressed image. According to an embodiment of the present disclosure, there is provided a device that receives an image via at least one camera or a communication interface, obtains a feature map for detecting a target object in the received image, outputs a compression ratio for correctly recognizing the target object in the image by inputting the image and the feature map to a deep neural network model composed of pre-trained model parameters, and generates a bitstream by compressing the image using the output compression ratio and encoding the compressed image.