11 results about "Video rate" patented technology

The application discloses a short video code rate self-adaptive method based on meta learning, relates to the technical field of streaming media, and comprises the following steps: S1, offline training, a model is established to represent user characteristics and network prediction information; S2, online learning, according to the characteristics of the current user environment, the model parameters are adjusted and optimized. The short video code rate self-adaptive method based on meta learning is adopted, a new SABR framework based on meta learning is successfully realized, the framework can quickly adapt to different user demands, the practicability and the calculation speed of the system are improved, and the framework has industrial application; the offline training and the online learning technology are successfully combined, the generalization and the stability of the model are enhanced; the idea of action masking is introduced in pre-training, the rationality and the reliability of decision are enhanced, the data amount required by meta learning is effectively reduced, the learning efficiency and the accuracy are improved, and the data demand and the training time in the industrial environment are significantly reduced.

A laser system for multiphoton microscopy comprises a seeder oscillator, fast modulator, amplifier, second modulator, and optical parametric amplifier configured to produce pulses with variable pulse spacing temporally synchronized to a scanner having sinusoidal motion. The variable pulse spacing ensures each pixel receives substantially the same number of laser pulses, eliminating uneven energy distribution of fixed repetition rate systems. The scanner operates at 4-12 kHz frequencies while the seeder oscillator produces femtosecond pulses at 10-100 MHz with 10-100 ns pulse spacing. The optical parametric amplifier outputs wavelengths from 600-1900 nm, with 1300-1700 nm optimized for three-photon microscopy. Methods include synchronizing pulse timing to scanner motion and varying interpulse intervals across scan lines. For 8 kHz scanners with 512 pixels per line, the system achieves 32% energy reduction compared to constant pulse spacing while maintaining 100-150 mW average power on biological samples, preventing thermal damage and enabling video rate imaging.

A method for performing content-based video compression using reinforcement learning (RL) is provided. The method includes obtaining frame information associated with a frame from a video. The frame information comprises quantization parameter (QP) information associated with the frame, and the QP information indicates an initial compression level for encoding aspects of the frame. The frame information and additional information are processed by an RL agent to generate a generated QP map indicating a plurality of updated values associated with a plurality of macro-blocks (MBs) of the frame. A bitstream is generated comprising a plurality of bits for the frame based on the generated QP map. Specifically, the plurality of updated values from the generated QP map indicates an amount of allocated bits from the bitstream to allocate for each of the plurality of MBs. The bitstream is provided to a downstream model.

This invention belongs to the field of video coding and decoding technology, and relates to a three-dimensional wavelet scalable video bitrate control method and system based on video content-aware characteristics. This method fully considers the impact of wavelet filter type, subband coupling phenomenon, and temporal subband content-aware characteristics on reconstruction error propagation, and proposes an adaptive Lagrange multiplier selection algorithm suitable for the wavelet domain. Then, an R-λ model based on video content-aware characteristics is established. Finally, based on the proposed bitrate control model, the coding parameters are adaptively adjusted to achieve the bitrate control target. Experimental results show that, compared with the bitrate control methods used in current classic three-dimensional wavelet video coding and decoding schemes, the method proposed in this invention achieves accurate bitrate control without significantly increasing complexity, while significantly improving the rate-distortion performance of the codec, better maintaining the continuity of video quality, and obtaining a more stable reconstructed video quality.

[Problem] To provide an imaging device and an imaging method with which it is possible to easily switch a double-speed setting without changing a clock and simplify a structure. [Solution] Provided are an imaging device that supports N-times faster imaging and an imaging method therefor, wherein an exposure control unit 7 causes a sensor 2 to be exposed in an exposure period in which exposure is performed at an M-fold speed equal to or less than the numerical value N of an N-fold speed for the video inputted from a lens. The exposure control unit 7 further exercises control by an exposure control signal so that a pixel signal is outputted in the same output period as the N-fold speed after the end of the exposure period and a given period after the end of the output period that corresponds to the numerical value M of an M-fold speed is made a disable period in which no pixel signals are outputted. A video rate conversion unit 5 outputs M frames of the M-fold speed video signal at the same timing as the N-fold speed.

The invention relates to a short video code rate self-adaption method and device, computer equipment and a storage medium. The method comprises the following steps: when an episode is triggered to be switched, acquiring a current application code rate applied to a previous episode, and predicting a current suggested code rate in combination with the current application code rate and a current network state, and because the suggested code rate predicted in the network state of the previous episode is stored in a prediction history queue, if the current suggested code rate is not consistent with the current application code rate, rapidly determining a target code rate adaptive to the current network in combination with a predicted historical queue dynamically generated by indicating the network characteristics of the previous episode and the current application code rate when the current suggested code rate is not consistent with the current application code rate, and taking the target code rate as the playing start code rate of the current episode after the episode is switched. Accurate play starting is carried out on the current episode in combination with the network state of the previous episode, and the accuracy of the play starting code rate in the play starting stage is ensured.

The invention relates to a liquid crystal display, in particular to a cholesteric display adopting surface and electric field induced vertical alignment (SEVA) and a vertical alignment multistable cholesteric liquid crystal display. The SEVA display is addressed by a TFT active matrix, providing not only video rate dynamic pictures, but also excellent still images.

The invention discloses an HDR video rate distortion optimization method and device based on adaptive distortion weight, equipment and a medium, and relates to the technical field of HDR video coding, and the method comprises the steps: determining each to-be-processed coding region of a current video frame in the coding process of a target HDR video, and calculating the region average brightness corresponding to each to-be-processed coding region; inputting the area average brightness into a pre-constructed distortion weight mapping model to obtain distortion weight factors respectively corresponding to the coding areas to be processed; the distortion weight mapping model is a piecewise linear model used for representing a relation between brightness and a distortion weight factor; and constructing a target rate-distortion cost formula based on the distortion weight factor, and applying the target rate-distortion cost formula to a rate-distortion optimization decision of the to-be-processed coding region so as to complete a coding operation on the to-be-processed coding region.

A method for performing content-based video compression using reinforcement learning (RL) is provided. The method includes obtaining frame information associated with a frame from a video. The frame information comprises quantization parameter (QP) information associated with the frame, and the QP information indicates an initial compression level for encoding aspects of the frame. The frame information and additional information are processed by an RL agent to generate a generated QP map indicating a plurality of updated values associated with a plurality of macro-blocks (MBs) of the frame. A bitstream is generated comprising a plurality of bits for the frame based on the generated QP map. Specifically, the plurality of updated values from the generated QP map indicates an amount of allocated bits from the bitstream to allocate for each of the plurality of MBs. The bitstream is provided to a downstream model.

Disclosed herein are embodiments of imaging biological specimens. An imaging system can include a microscope for directly viewing the biological specimen and a multi-spectral imaging apparatus for outputting digitally enhanced images, near-video rate imaging, and / or videos of the specimen. An imaging system can include a digital scanner that digitally processes images to produce a composite image with enhanced color contrast of features of interest.

A method for performing content-based video compression using reinforcement learning (RL) for video rate control for a downstream task is provided. The method includes processing frame information associated with a raw frame from a video using an RL agent to generate quantization parameter (QP) information that indicates one or more values associated with a compression level of the raw frame and encoding the raw frame into a bitstream based on the QP information. The method further includes reconstructing the raw frame using the bitstream to obtain a reconstructed frame and processing the raw frame as well as the reconstructed frame using a pre-trained downstream model to generate two outputs. The method then includes determining a downstream task reward based on the two outputs and training the RL agent based on the downstream task reward.