Provided are methods and devices for encoding and decoding using parameter sets, and electronic equipment. In the method for encoding, an encoder determines parameter sets and/or virtual parameter sets for a slice, wherein the virtual parameter set is a data structure which is generated by loading information acquired from a bitstream into a syntax structure of an existing parameter set and/or a preset syntax structure and includes tool parameters and/or control parameters; and the encoder writes identification number (ID) (s) of the parameter sets and/or virtual parameter sets into a bitstream. Using the method, encoding and decoding efficiency is improved.

Techniques are provided for adaptively controlling an encoding device to allow dynamic insertion intra-coded video content based on feedback information. For example, at least a portion of a video slice of a video frame in a video bitstream can be determined to be missing or corrupted. Feedback information indicating at least the portion of the video slice is missing or corrupted can be sent to an encoding device. An updated video bitstream can be received from the encoding device in response to the feedback information. The updated video bitstream can include at least one intra-coded video slice having a size that is larger than the missing or corrupted video slice. The size of the at least one intra-coded video slice can be determined to cover the missing or corrupted slice and propagated error in the video frame caused by the missing or corrupted slice.

A method in which a server recognizes and determines an object in an image may include calculating a feature map including an object feature point within the image by learning the image based on a first model, calculating compression feature information by compressing the feature map based on a first filter, inputting the compression feature information for each at least one layer and calculating respective prediction information by learning the compression feature information for each layer based on a second model, and calculating prediction result information by determining whether the respective prediction information sequentially exceeds a predetermined threshold value in order from a top layer.

A method in which a server recognizes and determines an object in an image may include calculating a feature map including an object feature point within the image by learning the image based on a first model, calculating compression feature information by compressing the feature map based on a first filter, inputting the compression feature information for each at least one layer and calculating respective prediction information by learning the compression feature information for each layer based on a second model, and calculating prediction result information by determining whether the respective prediction information sequentially exceeds a predetermined threshold value in order from a top layer.

A video processing pipeline comprises: a video frame partitioning module configured to divide video frames into frame portions; a change module configured to, for each frame portion of a second video frame, determine a difference between video data of the frame portion of the second video frame and video data of a corresponding frame portion of a first video frame; a probability module configured to determine a probability, that decreases with an increasing size of the difference, for individually suppressing each of the determined differences; a suppress module configured to decide whether to suppress each determined difference or not based on a respective determined probability, and to suppress the difference in case it is decided to suppress the difference; and an encode module configured to inter-encode the frame portions of the second video frame in relation to the first video frame.

There are provided methods and apparatus for adaptive geometric partitioning for video encoding and decoding. An apparatus includes an encoder for encoding image data corresponding to pictures by adaptively partitioning at least portions of the pictures responsive to at least one parametric model. The at least one parametric model involves at least one of implicit and explicit formulation of at least one curve.

A method includes receiving an input video stream and scaling the input video stream into two or more spatial layers. For each spatial layer, the method also includes generating a temporal layer prediction pattern by: obtaining a temporal base layer for a corresponding spatial layer; identifying, based on the temporal base layer, a plurality of temporal layers and a plurality of temporal time slots during a temporal period; and aligning the temporal base layer for the corresponding spatial layer with one of the temporal time slots during the temporal period. Each temporal time slot is associated with one of the temporal base layer or one of the plurality of temporal layers for the corresponding spatial layer. The temporal base layer for each corresponding spatial layer is aligned with a different temporal time slot than each other temporal base layer for each other corresponding spatial layer.

A method includes receiving an input video stream and scaling the input video stream into two or more spatial layers. For each spatial layer, the method also includes generating a temporal layer prediction pattern by: obtaining a temporal base layer for a corresponding spatial layer; identifying, based on the temporal base layer, a plurality of temporal layers and a plurality of temporal time slots during a temporal period; and aligning the temporal base layer for the corresponding spatial layer with one of the temporal time slots during the temporal period. Each temporal time slot is associated with one of the temporal base layer or one of the plurality of temporal layers for the corresponding spatial layer. The temporal base layer for each corresponding spatial layer is aligned with a different temporal time slot than each other temporal base layer for each other corresponding spatial layer.

A video encoding method comprises encoding a series of images of original video data into an encoded video stream comprising key frames and delta frames, wherein the delta frames are organized in a hierarchical prediction pattern comprising a plurality of temporal layers. The video encoding method further comprises adding to the encoded video stream a hidden delta frame for at least some of the key frames. Each hidden delta frame corresponds to a key frame, is based on same original video data as the corresponding key frame and is referring to a previous key frame in the encoded video stream. Also, a method of reducing a file size of the video stream encoded according to the video encoding method is disclosed.

A method for processing video data, comprising: receiving a stream of input video data representative of a number of successive frames generated by an imaging device's image sensor; selecting at least some of the frames; for each selected frame: determining, using an image signature algorithm, a signature for each region of the given selected frame; and, based on such signatures, classifying each region in that frame as either a changing or a static region; and generating an output video data stream that is a compressed version of the input video data, with a greater average data reduction rate for static region data than for changing region data, of the selected frames. The signature algorithm is such that a region's signature has substantially smaller size than the input data representative of that region, and such that signatures for visually similar regions are the same or similar.