1930 results about "Video transmission" patented technology

The invention discloses a real-time image video compression method for dynamic frame screening, and relates to the technical field of data compression and transmission, and the method comprises the steps: obtaining a power transmission line monitoring video stream, carrying out the importance analysis of a plurality of continuous frames through a multi-dimensional importance evaluation index, and generating an importance index; network bandwidth and computing power information are monitored in real time, and a screening threshold value is dynamically adjusted; and positioning the key frame and the redundant frame according to the comparison of the importance index and the screening threshold. The key frame is preferentially transmitted after being encoded by a preset encoder, and the encoder retains complete image information; and the redundant frame is subjected to lightweight compression or skipping processing through the differential processing module, and is transmitted after the key frame is transmitted. The technical problem that the stability and the real-time performance of video transmission are affected due to insufficient network bandwidth and equipment computing power resources in the prior art is solved, and the technical effects of reducing unnecessary data transmission and improving the real-time performance and the stability of video transmission are achieved.

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention relates to the technical field of traffic management, in particular to a video monitoring traffic intelligent optimization system which comprises a time sequence fragment evaluation module, a path priority adjustment module, a traffic scheduling optimization module and a buffer strategy adaptation module. According to the invention, the transmission efficiency and quality of the video monitoring data are improved by analyzing the video stream in real time and adjusting the frame rate and the compression rate, the importance of each time sequence segment in the video stream is evaluated, the frame rate is adjusted according to the importance, more resources are ensured to be obtained by important events, unnecessary data transmission is reduced, network congestion is effectively reduced, and the transmission efficiency of the video monitoring data is improved. The network transmission path is monitored in real time, and the data transmission proportion is adjusted, so that the video stream passes through the network path with higher quality more preferentially, the stability and speed of video transmission are enhanced, the priority and bandwidth allocation are dynamically adjusted, and the video stream with high priority is protected to ensure that the video quality of a key monitoring area is not influenced by network fluctuation.

The invention discloses a video transmission method and system based on streaming processing, and relates to the technical field of video transmission, and the method comprises the steps: collecting a video stream, carrying out the preprocessing, extracting a video frame, packaging the video frame into a picture stream, carrying out the data fragmentation, distributing an initialization path for fragmented data, and calculating an initial traffic proportion; and optimizing path selection and adjusting path flow distribution according to network feedback to obtain a path optimization result, and transmitting the fragmented data according to the path optimization result. Through multi-level feature extraction and key frame selection of video frames, fragmented packaging of video data is realized, a dynamic path optimization technology is combined, path selection and flow distribution are dynamically adjusted based on a Q learning algorithm and a luminescence firefly algorithm, the transmission efficiency is improved, video image processing and a dynamic network optimization technology are fully combined, and the video image processing efficiency is improved. And a brand new solution is provided for efficient and stable transmission of high-definition video streams.

The invention relates to the technical field of camera remote control, and discloses a camera remote control method and system based on portable WiFi. The method comprises the following steps: acquiring position information, coverage range and signal intensity of camera nodes, and constructing a distributed network topology structure; according to the distributed network topology structure, scene analysis is carried out on video content collected by a camera, and an optimal coding parameter configuration table is calculated; dividing the video frame into different priority regions according to the optimal coding parameter configuration table, and executing differential compression processing to form a video data packet with a priority mark; according to the method, video data packets with priority marks are distributed to a multi-level transmission queue, data transmission is scheduled according to the current bandwidth state of the portable WiFi, and an ordered transmission stream is generated, so that the computing load and energy consumption of each camera node are effectively balanced, stable video transmission and control response can still be kept under the condition of network fluctuation, and the video transmission efficiency is improved. And the video lagging rate and the control delay are obviously reduced.

The invention provides a monitoring video transmission method and system based on wireless communication, and relates to the technical field of monitoring video transmission, and the method comprises the steps: obtaining and preprocessing network state data, the network state data comprising bandwidth, time delay and packet loss rate; establishing a network state prediction model based on the LSTM neural network, and predicting a future network state according to the network state prediction model; based on the future network state, a multi-objective optimization algorithm is adopted to perform transmission path selection; and for the selected transmission path, video coding parameters are adjusted, the coding parameters comprise a code rate, a resolution ratio and a frame rate, and a self-adaptive transmission protocol is adopted to dynamically adjust the transmission rate according to a real-time network state. According to the method, different paths can be dynamically selected, network congestion or degradation is avoided, and the video transmission quality and rate are adaptively and flexibly adjusted according to the current network state, so that stable transmission of the video quality under different network conditions is ensured.

The invention discloses a network information age optimization method and system based on meta-deep reinforcement learning. The method comprises the following steps: establishing a video transmission model, and collecting current state data, initial reward information and action information; the method comprises the following steps: acquiring meta-features through a meta-feature extraction function, acquiring a next-step action by an Actor strategy network in combination with a current strategy and the meta-features, sending the next-step action back to a video transmission model to give next-step state data and reward information, evaluating a next-step action effect selected by the Actor strategy network through a Critic value network, and calculating to obtain an Actor strategy network strategy gradient. Meanwhile, updating a value function of the Critic value network, setting a shearing probability ratio mechanism, and performing strategy gradient shearing on the network; and dynamically updating network parameters in combination with a meta-learning algorithm. According to the invention, the overall utilization rate of network resources is improved, and data transmission failure and retransmission caused by network congestion are reduced.

The invention discloses a security and protection monitoring video real-time transmission method based on the Internet of Things, and relates to the technical field of security and protection monitoring video transmission, and the method specifically comprises the following steps: when a texture repetition region exists in an image frame, determining all macro blocks forming the texture repetition region in the image frame, and marking the macro blocks as texture repetition blocks; performing comprehensive analysis on each texture repetition block, and evaluating a candidate motion vector direction dispersion degree when an image frame in the security and protection monitoring video has a texture repetition area; based on the evaluation result, determining whether to classify each texture repetition block and whether to respectively match a corresponding motion vector selection strategy; and according to the matched motion vector selection strategy, respectively executing corresponding dynamic regulation and control operations. According to the method, the problem that the direction of the motion vector is discrete and uncontrollable in a texture repetition area is solved, dynamic regulation and control of a coding strategy and synchronous optimization of a decoding end are realized, and the image reconstruction precision and the intelligent analysis stability are improved.

According to the packet loss prevention data transmission method and device, the equipment and the storage medium provided by the invention, the network index data of the current network is acquired in response to the data transmission instruction; inputting the network index data into a network state prediction model to obtain a prediction result of the current network state; determining an FEC redundancy strategy according to the prediction result; according to the FEC redundancy strategy, performing redundancy coding on to-be-transmitted data to obtain a redundancy data packet; and sending the data to be transmitted and the redundant data packet to a target receiving end. According to the predicted network state, the number of redundant data packets can be dynamically adjusted, unnecessary redundant data transmission is reduced, and the bandwidth utilization rate is improved. Unnecessary bandwidth waste is reduced through adjustment of dynamic redundancy coding, it is ensured that enough fault-tolerant capacity is provided when the network is unstable, data integrity is effectively ensured, and meanwhile audio and video transmission fluency and stability are improved.

The invention relates to the technical field of wireless high-definition audio and video transmission, and discloses a plug-and-play wireless high-definition audio and video transmission method and system.The method comprises the steps that foreground space texture features and audio and voice segments are obtained, and an initial feature set containing space and semantic features is obtained through semantic correlation analysis; a unified representation vector is output through multi-modal feature fusion, and a dynamic importance score is obtained by determining time sequence consistency, calculating an importance weight and performing normalization; and when the score exceeds a threshold value, segmenting the video frame in real time to determine an attention focus area and optimize a boundary, thereby generating an attention weight matrix, preferentially allocating bandwidth and forming a partition differentiation compression result. And in combination with a network bandwidth state, protection is enhanced for a key stream, the priority and the bit rate are dynamically adjusted, and high-quality audios and videos are output through decoding and recombination. According to the invention, the bandwidth allocation and compression strategy can be dynamically optimized, the transmission quality of a key area is guaranteed when the bandwidth fluctuates, and the audio and video transmission efficiency and experience are improved.

The invention discloses a hierarchical traffic prediction-based multi-terminal video display adaptive method, which comprises the following steps of: acquiring network traffic, user behaviors and equipment state information in real time by constructing a multi-dimensional feature acquisition module, and generating a dynamic feature matrix containing traffic, interaction and performance features; processing the feature matrix by using a deep learning model, and generating and dynamically adjusting a network traffic grading prediction result; formulating an optimized video content distribution strategy based on a prediction result and equipment performance parameters, and coordinating multi-terminal resource distribution; through user behavior analysis and network state monitoring, a self-adaptive video display control strategy is generated, dynamic adaptation of video resolution, frame rate and playing logic is realized, a full-link collaborative optimization module is constructed, terminal, network and application layer resource allocation is coordinated, a video transmission and display strategy is optimized, and network flow prediction and multi-terminal interaction requirements are combined to realize multi-terminal interaction. And generating a dynamic video layout and content switching strategy.

The invention relates to the technical field of network transmission, in particular to a video data transmission method, which comprises the following steps of: based on a video transmission data packet, analyzing a sending time point of the video data packet, recording a sending moment and a serial number, checking a time stamp at a plurality of transmission nodes, and generating a time stamp data packet. According to the invention, by matching the timestamps and the serial numbers, marking the data packets and checking the transmission nodes, the security and reliability of data transmission are improved, errors and loss of the data packets in the transmission process are reduced, and the video quality is optimized by analyzing the video data, identifying abnormal image frames and adjusting transmission parameters. Compression parameters and server resource allocation are dynamically adjusted according to video contents and in combination with real-time user requirements, so that video quality is maintained under the condition that the bandwidth is limited, the size of a buffer area and the capacity of a data packet are adjusted according to network conditions, and the fluency and continuity of video playing are improved.

The invention discloses a network optimization method based on an online conference, which relates to the technical field of real-time audio and video transmission, and comprises the following steps: in a transnational network, deploying monitoring probes at nodes participating in the online conference for monitoring network parameters such as network delay, bandwidth, packet loss rate and jitter in real time; the method comprises the following steps: processing network parameters and predicting abnormity by using a statistical analysis algorithm and a time sequence model to obtain network condition information, and then combining global network topology and using a reinforcement learning Q-Learning model and a graph neural network GNN topology prediction algorithm; according to the method, the network condition is monitored and predicted in real time, the optimal transmission path is output by using the reinforcement learning Q-Learning model and the GNN topology prediction algorithm, the bandwidth fluctuation is predicted through the LSTM, the packet loss rate is predicted based on the Bayesian network, the data transmission strategy is optimized, the network delay, the packet loss rate and the jitter are effectively reduced, and the network performance is improved. And the smoothness and the stability of the conference are improved.

The invention relates to a control method based on a cloud PC server and a cloud PC management platform. The method comprises the following steps: starting the cloud PC management platform, and establishing and keeping link between a computing blade and the cloud PC management platform through a host blade management module; a user logs in a preset host through an access layer of the cloud PC management platform, and then remotely accesses the preset host by establishing an audio and video transmission channel and responding to a keyboard and mouse event; every preset time interval, the cloud PC management platform detects the working state of the cloud server through the equipment management module and updates the database; and if the user has an upgrading demand, accessing the computing blade through the host blade management module to execute upgrading. By configuring a special audio and video channel, the audio and video experience of a user is improved, and meanwhile, the stability of the cloud server is improved by detecting the working state of the cloud server in combination with the equipment management module and upgrading the computing blade by the host blade management module.

The invention discloses an efficient real-time voice and video transmission method, which is characterized in that a proper video frame coding mode is selected according to the state of video content, so that a system can effectively reduce the data volume and save the network bandwidth by using a high-compression-rate coding mode in a static state; more video details are reserved by using a low-compression-rate coding mode in a dynamic state, so that the data transmission quantity is reduced to the greatest extent on the premise of not influencing the video quality, and the overall transmission efficiency is improved; meanwhile, due to the fact that the transmitted data size is reduced and the data transmission speed is increased, transmission delay of the audio and video data is reduced, the phenomenon of audio and video asynchronization caused by data packet disorder can be effectively reduced through reasonable buffer area management, it is ensured that the audio and video data are played according to the generated time sequence, the watching experience of a user is optimized, and the user experience is improved. High-quality and low-delay audio and video transmission service is realized, and high requirements of users on real-time performance and definition are met.

The present application relates to the technical field of communications, and discloses an adaptive video transmission method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a wireless channel environment parameter and a wireless scheduling parameter of a current period; inputting the wireless channel environment parameter and the wireless scheduling parameter of a preset target time sequence into a converged QoE prediction neural network model, and predicting a QoE measurement index for video transmission within a future preset duration; and on the basis of the predicted QoE measurement index, performing optimization adjustment on the wireless scheduling parameter of the next period to make the QoE measurement index of the next period reach a preset QoE standard for transmission.

The invention discloses a semantic analysis model training method, a video data transmission method, electronic equipment and a computer readable storage medium, and belongs to the technical field of artificial intelligence and video transmission. According to the training method, a multi-task fusion network is constructed based on a MobileViT-S architecture, and target, action and scene semantic information in a video is synchronously extracted in combination with a lightweight Transform and a convolution module. Training adopts a two-stage strategy; in a pre-training stage, a general data set is used for optimizing the basic capability; in the fine tuning stage, curriculum learning and time sequence smooth loss are introduced, complex ocean scene sample weights are gradually increased, a multi-task loss function is jointly optimized, and weight balance precision and bandwidth adaptation are dynamically adjusted. Deploying the model at a ship end to extract structured semantic information; a shore base restores a video through a GAN + Diffusion model, and the time sequence consistency is improved in combination with optical flow interpolation and Kalman filtering. Therefore, the problem of video real-time transmission in a low-bandwidth scene is solved, the method can be applied to the fields of ship monitoring, remote cooperation and the like, and the transmission efficiency and reliability are remarkably improved.

The invention relates to the technical field of video monitoring communication, in particular to a 5G high-definition video monitoring method and system for a smart city, and a medium, and the method specifically comprises the steps: obtaining a 5G high-definition video based on the moving features of each feature point of a moving target between each frame and a previous frame image in a monitoring video, and the gray gradient change condition in each channel image of an RGB image of each video frame; constructing an information rich characteristic value of each video frame; calculating a bandwidth influence coefficient of each detection time period based on a data fluctuation abnormal condition of the network bandwidth data in each preset detection time period; constructing a video transmission quality coefficient by combining the features, optimizing a quantization parameter of a video coding algorithm in the current detection time period, carrying out coding transmission on the monitoring video in the current detection time period, and carrying out intelligent analysis on the transmitted monitoring video; the problem of frame loss or low video quality of video content with relatively high potential information value is reduced; and the interference on subsequent video monitoring intelligent analysis and target detection can be reduced.

The invention discloses a mining front-end AI video computing power terminal device comprising a video coding module based on deep learning, and the video coding module is used for improving the efficiency of a data compression algorithm under a low-bandwidth network condition; the video acquisition module is used for capturing video data; the data processing module is used for analyzing and optimizing the received video data in real time; the transmission control module is used for adjusting a video transmission strategy to reduce delay; and the feedback processing module is used for dynamically adjusting a coding parameter function based on feedback information so as to further solve the problem of video transmission delay. According to the invention, the problem of video transmission delay caused by low efficiency of a data compression algorithm under a low-bandwidth network condition can be solved.

The embodiment of the invention relates to the technical field of video transmission, and particularly discloses a multi-mode method for real-time high-fidelity video transmission based on a semantic stream. According to the embodiment of the invention, the method comprises the steps: receiving a multi-frame video sequence, extracting spatial and temporal correlation through a space-time compressor, and outputting a potential representation; mapping the potential representation to a semantic space through a semantic translator, and gradually converting visual features and text features; performing multi-modal fusion on the potential representation and the text features through a preset Transform fusion model, and outputting a fusion representation; quantizing the fused representation into a quantized representation through a preset codebook model; and processing the quantized representation and the text features through a video control network to generate a reconstructed video sequence. According to the method, the compression efficiency can be remarkably improved, and priority transmission of key semantic information is realized, so that high-efficiency compression and perceptual correlation are kept under the condition that the bandwidth is limited, and semantic consistency and time coherence of video contents can be ensured.

The invention relates to the technical field of force operation robots, in particular to a VR video return system and method based on a bionic operation robot, and the system comprises a bionic operation robot end and a VR end, and the bionic operation robot end comprises a video collection module, a video preprocessing module, a video coding module and a communication transmission module. The VR end comprises a communication receiving module, a video decoding module and a VR display and interaction module. According to the immersive experience provided by the VR technology, an operator can observe the working environment and the operation target of the bionic operation robot more intuitively and accurately, so that an operation instruction is sent more accurately. For example, in the operation of the bionic robot, an operator can see the detailed situation of the transformer substation through VR video return, and the bionic operation robot is accurately controlled to carry out fine operation.

The invention relates to a method for dynamically adjusting parameters of a video encoder, which comprises the following steps of: acquiring a visual characteristic index corresponding to a video frame image in real time, and calculating an image complexity score of the image according to the visual characteristic index; the visual feature indexes comprise motion intensity, edge density and information entropy; acquiring a network index of a video transmission network in real time; the network indexes comprise available bandwidth, delay, jitter and packet loss rate; obtaining a current encoder control parameter according to the current image complexity score and the network index; the control parameters of the encoder comprise GOP, GP, FPS and Buffer Size; and adjusting the encoder according to the obtained control parameters of the encoder. The method is superior to an existing fixed parameter coding system in the aspects of image quality, delay control, system robustness and adaptability.

The invention particularly relates to a mobile camera management system capable of adaptively adjusting video coding quality. According to the mobile camera management system capable of adaptively adjusting the video coding quality, a network condition monitoring and predicting module is used for monitoring the current network bandwidth and packet loss rate in real time and predicting the network state; the video coding control module is used for dynamically adjusting video coding parameters according to the current network bandwidth and the ROI; and the adaptive adjustment strategy module is responsible for issuing a video coding strategy to the video coding control module according to the data fed back by the network state monitoring and prediction module so as to realize dynamic adjustment of the video coding quality. The mobile camera management system capable of adaptively adjusting the video coding quality can avoid video lagging and frame loss when the network bandwidth is relatively low, is suitable for various mobile devices such as a vehicle-mounted device, an unmanned aerial vehicle and a wearable camera, effectively improves the continuity and availability of video transmission, and realizes stable application in an environment with unstable signals.

A transmitter includes a distributor that receives a stream of digital video samples and distributes these samples into vectors in a buffer per a permutation. A digital-to-analog converter (DAC) per vector receives from its corresponding vector the digital video samples and converts the digital video samples into analog video samples. Each source driver includes a collector that receives analog video samples from each DAC and stores the analog video samples of the corresponding vector, and amplifiers that receive the stored analog video samples in parallel from the collector and amplifies the stored analog video samples onto a column of the display panel. An AR / VR headset uses analog video transport to its processor, and from the processor to a display of the visor. Analog video transport is used between a camera and processor and between processor and display. Analog video is transmitted to or within the headset wired or wirelessly.

The invention provides a user experience optimization method and system for an audio and video transmission system, and the method comprises the steps: firstly obtaining a transmission quality data set in the audio and video transmission process of a target terminal, the transmission quality data set comprises a plurality of transmission session sequences, and each transmission session sequence is composed of network fluctuation, coding and decoding parameters, user feedback and other features; performing transmission feature extraction on the data set to obtain audio and video synchronization, dynamic code rate and decoding delay features, calling a pre-trained reinforcement learning optimization model, performing dynamic aggregation processing on the features to generate a transmission quality evaluation result, and finally determining a dynamic transmission optimization strategy based on the transmission quality evaluation result. And the real-time transmission parameter adjustment operation is triggered, so that the user experience of the audio and video transmission system is optimized.

The invention discloses a video stream non-perception encryption and decryption method based on an IP (Internet Protocol). The method comprises the following steps: acquiring a plaintext data stream, splitting the plaintext data stream into data packets, transmitting the data packets by using a UDP (User Datagram Protocol), introducing a forward error correction code, recombining the transmitted data packets, and generating an original plaintext video stream; carrying out network flow feature extraction on the plaintext data flow to obtain network flow features, carrying out load optimization to obtain a first network flow feature, carrying out encryption by using an encryption algorithm, and attaching an authentication tag to the end of the encrypted first network flow feature to obtain a second authentication tag; embedding the encrypted first network traffic feature into a data packet of the original plaintext video stream to obtain an encrypted data packet; the authentication tag is recalculated and compared with the second authentication tag, and the encrypted data packet is decrypted by using the corresponding key to obtain the original plaintext video stream after recombination, so that network attack can be effectively prevented, and the security of video transmission is guaranteed.

The invention provides a VR (Virtual Reality) video adaptive code rate control method based on multi-agent reinforcement learning, which relates to the technical field of VR, and comprises the following steps: acquiring historical video block transmission data prediction bandwidth and evaluating network stability, selecting probability distribution based on an Actor network output code rate of a multi-layer perceptron structure, and determining the probability distribution of the multi-agent reinforcement learning. Structural similarity and re-buffering indexes are calculated for the video blocks to construct a comprehensive reward function, local observation information is acquired by utilizing a multi-agent joint state vector, group information is integrated through attention weight, and network parameters are updated by adopting a strategy gradient method and an Adam optimizer to realize code rate optimization. According to the invention, the VR video transmission quality can be effectively improved, the re-buffering probability is reduced, the continuity and fluency of user watching experience are ensured, the code rate switching frequency is reduced, and the system stability is improved.

The invention belongs to the technical field of audio and video transmission, and relates to an audio and video transmission method and system with a self-adaptive network bandwidth, and the method comprises the steps: collecting audio and video signals through an audio and video coding assembly, generating coding data based on preset coding index parameters, and storing the coding data in an audio and video coding cache; millisecond-level real-time bandwidth sensing is realized through a local dynamic bandwidth sampling technology, so that the dependence on feedback of a receiving end is thoroughly eliminated, and the lagging phenomenon caused by delay is radically reduced; and a multi-level network state discrimination mechanism is constructed by further combining the two indexes of the data transmission cache utilization rate and the transmission rate, the network state is refined into four levels, and the strategy is dynamically adjusted, so that the congestion control response precision is remarkably improved. Under a network congestion scene, through intelligent filtering of non-key frames, shortening of an I-frame interval and reduction of redundant data transmission, key picture continuity and audio stream continuity are guaranteed preferentially, and the problems of network burden and service quality reduction caused by traditional full-amount transmission are solved.

The invention relates to the technical field of wireless streaming media, in particular to a real-time video transmission method and system based on 5G, and the method comprises the following steps: based on mobile terminal equipment, analyzing the signal strength and service bearing change of a 5G base station, screening an optimal access sequence, comparing channel capability with video partition characteristics, and adjusting coding parameter distribution. And analyzing link capability and packet loss performance, and optimizing uplink and downlink compression parameters to obtain a compression change trend. According to the invention, by dynamically identifying the signal association and network load change between the mobile terminal and the 5G base station, the multi-dimensional access priority sequence is generated in combination with the real-time bandwidth demand, the coding and allocation parameters are flexibly adjusted according to the video partition content characteristics and the wireless channel adaptation condition, and the access priority sequence is optimized in combination with the uplink and downlink performance and the frame type change. Dynamic regulation and control and synchronous updating of compression parameters are automatically completed, continuous transmission of video streams in a complex network environment is achieved, and the end-to-end consistency of video content and the integrity of picture data are improved.

A video encoder, decoder, system, and method for coding a video are presented. The video encoder for encoding a video is configured to encode frames of the video using intra coding and differential inter-frame coding into a data stream, measure a quality loss resulting from coding loss, and signal, as side information, information on the quality loss in the data stream. Further is presented a video decoder configured to decode frames of the video using intra decoding and differential inter-frame decoding from a data stream into which the video is encoded at a coding loss, estimate information on a quality loss resulting from the coding loss, and output, at an output interface of the video decoder, meta data revealing the information on the picture loss, and / or modify a reconstructed version of the video.