Frames Of Video Stream Research Articles

SUPPRESSION OFN THE VIDEO STREAM FRAMES PROCESSED BYUNMANNED SYS-TEMS USING FPV CONTROL

Using packet networks for first-person control of unmanned systems arises a problem of large transmitted data volumes . The largest volume of traffic during first-person control is presented by video stream frames. So, to improve the efficiency of the communication network between unmanned systems and external pilot station, it is necessary to compress video stream frames. A high compression degree can be provided by using variational autoencoders. One of the problems of using variational autoencoders for frame compression is the occurrence of specific artifacts in frames. This article proposes methods for suppressing the occurrence of artifacts when restoring frames from the latent space by a neural network decoder, as well as an empirical scale for assessing autoencoder artifacts. The approach proposed encompasses preparing pixel data of a video stream frame for encoding and further reconstruction after decoding. It is experimentally shown that one of the proposed methods allows eliminating the absolute majority of artifacts without introducing significant distortions into the reconstructed frames.

Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification

Abstract. Traditional indoor positioning technologies mostly require advanced installation of hardware devices, resulting in high costs and long-term maintenance. With advancements in image recognition and deep learning technologies, indoor visual positioning based on image recognition has become increasingly mature. This method offers the benefits of low cost and does not require additional hardware installation. However, it still has inherent defects, such as cumbersome data collection, complex algorithms, and universality. To minimize indoor information pre-collection cost, improve versatility, and enable rapid deployment in low-performance mobile devices, this paper proposes a lightweight indoor positioning system based on multiple self-learning features and key frame classification. The system is divided into two stages: preprocessing and real-time positioning. In the preprocessing stage, image information is collected for the entire indoor environment, and a key-frame recognizer is trained based on the image information. Simultaneously, an environmental feature information database is established. In the real-time positioning stage, the system first uses mobile devices such as smartphones to obtain real-time video streams. A key frame recognizer based on convolutional neural networks identifies key frames in each video stream frame, thereby obtaining approximate positions for rough positioning. Second, feature points are identified in each frame of the video stream and matched with feature points with location information in the built environmental feature information database to calculate precise positions for fine positioning. It has significant optimizations compared with conventional visual solutions in terms of preprocessing data collection, algorithm performance consumption, and versatility.

Results of a Study for Spatiotemporal Cloudiness Emission Structures in the LWIR Range

The results of experimental studies for the spatiotemporal structures of cloudiness emission in the LWIR (long-wave infrared) range using thermal imaging equipment are presented. Video streams with a minimum frequency of 50 Hz lasting more than 40 minutes, representing a sequence of digital thermal images for the sky fixed area, were used as initial data. In the process of studying, the spatiotemporal structures of cloudiness emission, time intervals (correlation radii) were estimated, the cross-correlation coefficients between frames of video streams being decreased from 1 to 0.5 during them. It has been established that for weather situations characterized by the cumulus cloudiness amount presence of (1–3) points, (4–6) points, and (7–9) points, the correlation radii are (45–75) s, (3–4) min and (5–6) min, respectively. For other types of atmospheric background (with altocumulus, cumulonimbus cloudiness and clear sky), the correlation radius exceeded the video recording time, i.e. such weather situations have a low rate of complete renewal for the cloudiness form.

A novel AI‐powered IR‐Visible dual camera system for measuring and tracking ocular surface temperature

Aims/Purpose: To report the development of a novel artificial intelligence (AI) powered, dual camera (infrared (IR)/visible) system capable of measuring and tracking ocular surface temperature (OST) over any time period.Methods: The system consists of an IR camera (Teledyne FLIR IR A655sc) and a visible (V) camera (FLIR BFS 51S5C‐C), co‐mounted on a slit‐lamp for simultaneous and overlapping fields of view (FOV); designed control algorithms; computer hardware and connecting cables. Novel algorithms are leveraged to synchronize IR and V video streams of exposed ocular surface and adnexa for image and video registration. Localisation of the cornea in V video stream obtained by deep learning (DL) AI algorithms designed for semantic segmentation of the cornea. Coordinates of V video stream segmented cornea are then used to extract corresponding OST data from the IR video stream in each video stream frame. Data analysis algorithms are used to extract OST data from IR video stream. Further image segmentation algorithms isolated specific areas of interest (AOI) for OST analysis.Results: The DL algorithm was trained using V images captured using the V camera. Image registration and segmentation errors were calculated to determine accuracy of system. Mean square error for registration was 5.03 ± 1.82 (c.0.45 mm). Mean Intersection over Union (IoU) was 97.6%, representing accuracy in identifying corneal and scleral pixels in tracked eye segmentation. Analysis software extracted rate of OST change and relative OST change compared to baseline across cornea and selected AOI.Conclusions: A novel AI‐powered system for measuring and tracking OST over time was developed. The system synchronously records IR and V video streams of the eye surface and automatically extracts OST over time. The system can track eye movements and remove artefact eyelid blink frames from the data. A consistent AOI, i.e., pupil, whole cornea, inferior half, superior half, or selected corneal region, can be selected for OST extraction and analysis over time. Experimental results show that the system can track and analyse OST change over time.

АВТОМАТИЧЕСКОЕ РАСПОЗНАВАНИЕ ПОВЕРХНОСТНЫХ ДЕФЕКТОВ СВАРКИ ПРИ ПРОИЗВОДСТВЕ ГИБКИХ ТРУБ ИЗ НЕРЖАВЕЮЩЕЙ СТАЛИ

The paper describes the automatic recognition of surface welding defects on video stream frames while manufacturing flexible stainless steel pipes. The defects that appear during automatic tungsten inert gas welding are studied. A universal algorithm based on the background subtraction method is presented for detecting and localizing various welding defects on video stream frames. A solution to the problem of recognizing such defects as lack of fusion and scaled surface is also proposed. A dataset containing texture features of defects was created. Classifiers were trained using the support vector machine. Experimental studies have shown that the proposed solutions allow for prompt detection of any deviations occurring during metal welding and recognize certain welding defects on the processed frames of the video stream.

A learnable end-edge-cloud cooperative network for driving emotion sensing

The driver's emotional state directly affects safe driving. Under the "vehicle-human-road-cloud" integrated control framework, we propose an end-edge-cloud collaborative emotion perception network model (EEC-Net). The end side extracts the key frames of the driver's face video stream and performs batch compression; the edge side extracts the region of interest (ROI) of the reconstructed images as the input of the emotion recognition model (tiny_Xception) for classification; the cloud control terminal receives abnormal ROI image data and performs online training to dynamically adjust the operating parameters of the edge model. Finally, we test on open and self-built datasets, and the results show tiny_Xception has a significant improvement in accuracy of 2.45% compared to mini_Xception; the EEC-Net model can reliably perceive the negative emotion period, and the overall system memory consumption is reduced by about 5%, and the network transmission data volume and the computation time of emotion recognition are reduced by 95%, 60% respectively.

Means and Methods of Efficiency Estimation of Video Stream Transmission Based on GigE Vision Technology Using Application Processor

The paper investigates the possibility of efficient implementation of a GigE Vision compatible video stream source on a computing platform based on a system-on-a-chip with general-purpose ARM processor cores. In particular, to implement the aforementioned video source, a proprietary prototype of a GigE Vision compatible camera was developed based on the Raspberry Pi 4 single-board computer. This computing platform was chosen due to its widespread use and wide community support. The software part of the camera is implemented using the Video4Linux and Aravis libraries. The first library is used for the primary image capturing from a video sensor connected to a single board computer. The second library is intended for forming and transmission of video stream frames compatible with GigE Vision technology over the network. To estimate the delays in the transmission of a video stream over an Ethernet channel, a methodology based on the Precise Time Protocol (PTP) has been proposed and applied. During the experiments, it was found that the software implementation of a GigE Vision compatible camera on single-board computers with general-purpose processor cores is quite promising. Without additional optimization, such an implementation can be successfully used to transmit small frames (with a resolution of up to 640 × 480 pixels), giving a delay less than 10 ms. At the same time, some additional optimizations may be required to transmit larger frames. Namely, a MTU (maximum transmission unit) size value plays the crucial role in latency formation. Thus, to implement a faster camera, it is necessary to select a platform that supports the largest possible MTU (unfortunately, it turned out that it is not possible with Raspberry Pi 4, as it supports relatively small MTU size of up to 2000 bytes). In addition, the image format conversion procedure can noticeably affect the delay. Therefore, it is highly desirable to avoid any frame processing on the transmitter side and, if it is possible, to broadcast raw images. If the conversion of the frame format is necessary, the platform should be chosen so that there are free computing cores on it, which will permit to distribute all necessary frame conversions between these cores using parallelization techniques.

Correlation Properties of Sea Surface Images on Video Stream Frames

The work is devoted to the characteristic properties analysis of the sea surface image on video stream frames, which make it possible to make a decision about the detection of object images on them in the absence of a priori information about objects, which is important in the development of navigation systems, security and surveillance systems. In this paper, it is proposed to analyze the values of the normalized cross-correlation coefficients of a given image fragment with higher dimension fragments on sequential video stream frames to identify the distinctive properties of agitated sea surface and floating objects images. The computational experiments were carried out and have shown that the analysis of the results of calculating the frame fragments cross-correlations allows us to estimate the amount of displacement and distortion of a given image fragment. The results of computational experiments demonstrate the presence of differences in the values of the corresponding cross-correlation coefficients for the sea surface images with different agitation degrees, containing and not containing an image of the object. Based on the analysis of the proposed correlation properties of the sea surface images, a decisive rule for selecting fragments of frames containing an image of an object is formulated, the use of which, in many cases, allows to detect fragments of object images correctly.

Multilevel algorithm for color clustering of images

The fast multilevel algorithm to cluster color images (MACC – Multilevel Algorithm for Color Clustering) is presented. Currently, several well-known algorithms of image clustering, including the k‑means algorithm (which is one of the most commonly used in data mining) and its fuzzy versions, watershed, region growing ones, as well as a number of new more complex neural network and other algorithms are actively used for image processing. However, they cannot be applied for clustering large color images in real time. Fast clustering is required, for example, to process frames of video streams shot by various video cameras or when working with large image databases. The developed algorithm MACC allows the clustering of large images, for example, FullHD size, on a personal computer with an average deviation from the original color values of about five units in less than 20 milliseconds, while a parallel version of the classical k‑means algorithm performs the clustering of the same images with an average error of more than 12 units for a time exceeding 2 seconds. The proposed algorithm of multilevel color clustering of images is quite simple to implement. It has been extensively tested on a large number of color images.

NRSfPP: non-rigid structure-from-perspective projection

A state-of-the-art algorithm for perspective projection reconstruction of non-rigid surfaces from single-view and realistic videos is proposed. It overcomes the limitations arising from the usage of orthographic camera model and also the complexity and non-linearity issues of perspective projection equation. Unlike traditional non-rigid structure-from-motion (NRSfM) methods, which have been studied only on synthetic datasets and controlled lab environments that require some prior constraints (such as manually segmented objects, limited rotations and occlusions, and full-length trajectories); the proposed method can be used in realistic video sequences. In addition, contrary to previous methods that use multiple cameras with different relative viewing angles, only a single-view video is required to reconstruct the 3D structures. By only using the 2D frames of incoming video stream, the proposed method extracts the projective depth coefficients of each point in each input frame, rotation matrix, translation vector, varying camera parameters (such as focal lengths for each input frame), and finally reconstructs the 3D deformable shape. Due to the high number of unknowns, the problem has been divided into two parts of projective depth coefficients extraction and 3D shape reconstruction. Perspective reconstruction of non-rigid surfaces has been extended to be certainly converged which leads to a significant increase in execution frequency of the iterated algorithm that has been presented for projective depth coefficients extraction. As such, it produces promising results for perspective projection reconstruction of non-rigid surfaces from single-view and realistic videos. The accuracy and robustness of the proposed method is demonstrated quantitatively on synthetic data and qualitatively on real image sequences. The experimental results show that NRSfPP provides the state-of-the-art results and resolves the failures of previous approaches by eliminating all of the predefined situations and constraints of applying perspective projection as the camera model.

Barannik V.V., Ryabuha U.N., Tverdokhlib V.V., Shulgin S.S., Barannik D.V. VIDEO SEGMENT CODING METHOD FOR BIT RATE CONTROL INFORMATION TECHNOLOGY

The conceptual bases of construction of an effective coding method as a part of the module of control of bit speed of video traffic in system of video data processing at source level are considered. This method reduces the number of bits on the description of video frames by reducing a number of redundancies - namely - structural and combinatorial, which in most cases when processed by traditional methods remain unchanged. This is achieved by identifying the lengths of a series of binary elements in the binary description of the DCT transformant. In this case, the peculiarities of the localization of binary series are taken into account, namely, that the maximum number of zero elements of the binary description can be detected within the high-frequency zone of the transformant, and within the lower bits of the components. The essence of using the proposed encoding method in controlling the bit rate of the video stream is revealed, namely, the principles of constructing a code representation of a frame fragment and approaches to determining the structural units of an individual video frame within which control is performed. The method focuses on the processing of the bit representation of the discrete-cosine transform transformant, and at this stage of the transformant processing is considered as a structural component of the video stream frame, at the level of which the encoding is performed. At the same time, to ensure the flexibility of controlling the bit rate of video traffic, for each of the transformants of the discrete-cosine transformation is decomposed to the level of the plurality of bit planes. In addition, this principle of forming a code representation of a fragment of the video stream allows you to control the level of error that may be introduced in the process of bit rate control. Because the proposed approach provides for bit rate control by manipulating the number of bit planes that make up the description of the transformant, the error rate can be controlled by excluding only those bit planes that least contribute to reducing data integrity..

Computational Method for Recognizing Situations and Objects in the Frames of a Continuous Video Stream Using Deep Neural Networks for Access Control Systems

An effective (performance- and accuracy-wise) computational method for pattern recognition in a continuous video stream using deep neural networks for access control systems is proposed. The class of recognition problems solved by the method using a sequence of video stream frames is identified: the vehicle itself and the characters on its license plate (LP), faces of people, and abnormal situations. In contrast to the known solutions, a classification with a subsequent reinforcement based on multiple frames of a video stream and with an algorithm for the automatic annotation of images is used. Neural network architectures with independent recurrent layers for classifying video fragments adapted for the problems, a dual network for face recognition, and a deep neural network for vehicle character recognition are proposed. New databases for neural network training are created. A schematic diagram of an intelligent access control system for ensuring the security of an enterprise, a distinctive feature of which is the use of a multirotor unmanned aerial vehicle with a computing unit, is proposed. Field experiments are carried out, and the accuracy and performance of the computational method in solving each problem are assessed. Software modules in the Python language for solving tasks of the intelligent access control system are developed.

Technical vision system to ensure the movement of a group of multicopters

The actual task of controlling a group of multicopters performing coordinated actions and are locating at short distances from each other, cannot be performed with the help of a standard on-board autopilot on GPS or GLONASS signals, which give large errors.
 The solution to this problem is possible due to additional equipment that allows you to set the distance between the multicopters and their relative position. To do this, it is proposed to mark each multicopter with an image label in the form of a standard geometric figure or a geometric body of a given color and size, and to use technical vision system and image recognition algorithms.
 The structure of the technical vision system for the multicopter was developed and algorithms for image processing and calculation of the change of coordinates of the neighboring multicopter, which are transmitted to the control system to introduce the necessary motion correction, were proposed.
 The method to identify the reference object in the image of the scene by its color was used in this work. This method is very effective compared to other methods, because it requires only one pass per pixel, which gives a significant advantage in speed during video stream frame processing. RGB color model with a color depth of 24-bit was chosen based on the analysis. Since the lighting during the flight can change, the color is set by the limits of change of the components R, G, B.
 To determine the distance between multicopters, a very simple but effective method of determination the area of the recognition object (labels on the neighboring multicopter) with next comparation it with the actual value is used. Since the reference object is artificial, its area can be specified with high accuracy.
 The offset of the center of the object from the center of the frame is used to calculate the other two coordinates.
 In the beginning, the specific camera instance is calibrated both for a known value of the area of the object and for its displacement along the axes relative to the center of the frame.
 The technical vision system model in the Simulink software environment of the Matlab system was created to test the proposed algorithms. Based on the simulation results in Simulink, you can generate code in the C programming language for further implementation of the system in real time.
 A series of studies of the model was conducted using a Logitech C210 webcam with a 0.3 megapixel photo matrix (640x480 resolution). According to the results of the experiment, it was found that the maximum relative error in determining the coordinates of the multicopter did not exceed 6.8 %.

Frame interpolation in video stream using optical flow methods

The article deals with the issues of interpolation and extrapolation of video stream frames in real time. Results of research of the method of generation of intermediate frames of a video stream are given. The method is based on the optical flow calculate. The results of comparison of implementations of the method using the Farneback, Brox and Duality based TV-L1 approaches for the calculation of optical flow and CUDA technology are presented. It is shown that the Farneback method best result is gives. The Duality based TV-L1 method shows a smooth and high-quality optical flow, but gives a very slow calculation speed. The Brox method is not suitable at all, since it shows the worst result both in terms of calculation speed and optical flow quality. The results of comparison of implementations based on CUDA and OpenCL technologies are also presented. OpenCL implementations are almost two times slower than CUDA implementation. This leads to the inability to generate frames in 60 FPS in real time even for low resolution images. However, CUDA can work only on NVidia GPU accelerators. In conclusion, the ways of further research on this topic based on the use of deep neural networks are presented.

ТЕХНОЛОГІЯ КОДУВАННЯ ТРАНСФОРМАНТ В СИСТЕМАХ УПРАВЛІННЯ БІТОВОЮ ШВИДКІСТЮ ВІДЕОПОТОКУ......

The conceptual basements of constructing an effective encoding method within the bit rate control module of video traffic in the video data processing system at the source level are considered. The essence of using the proposed method in the course of the video stream bit rate controlling disclosed, namely, the principles of constructing the fragment of the frame code representation and approaches for determining the structural units of the individual video frame within which the control is performed. The method focuses on processing the bit representation of the DCT transformants, and at this processing stage transformant was considered as a structural component of the video stream frame at which the encoding is performed. At the same time, to ensure the video traffic bit rate controlling flexibility, decomposition is performed with respect to each of the transformants to the level of the plurality of bit planes. It is argued that the proposed approach is potentially capable to reducing the video stream bit rate in the worst conditions, that is, when component coding is performed. In addition, this principle of video stream fragmen code representation forming allows to control the level of error that can be made in the bit rate control process. However, in conditions where the bit representation of the transformant is encoded, the method is able to provide higher compression rates as a result of the fact that the values of the detection probability of binary series lengths and the values of detected lengths within the bit plane will be greater than in the case of component coding. This is explained by the structural features of the distribution of binary elements within each of the bit planes, which together form the transformer DCT. In particular, high-frequency transformer regions are most often formed by chains of zero elements. The solutions proposed in the development of the encoding method are able to provide sufficient flexibility to control the bit rate of the video stream, as well as the ability to quickly change the bit rate in a wide range of values.

Usage of characteristic points to reduce the time of performance of SAD metric calculation during video flow compression by motion compensation method

Today, it is difficult to imagine a video system that does not use compression of the input video stream for transmission or storage, because the uncompressed size of video files can reach several hundred gigabytes. There are many different video stream compression techniques used in various codecs, however, one of the most commonly used is the motion compensation technique, which allows you to transmit video stream frames in the form of compensated inter frame difference, by finding the motion vectors of individual blocks image. Despite the fact that the above-mentioned technique has been used for the last twenty years, it still has two problem-priority places - assessments of the similarity of image blocks and the algorithm for searching blocks. This article focuses on the problem of estimating the similarity of the image, in order to reduce the time of estimating the similarity of the blocks, which in modern algorithms takes from 40 to 80% of the time of the entire process of encoding the video stream. The article considers the usage of metrics for estimating the similarity of images and their importance for the process of motion compensation during video stream compression, such as SSD (standard deviation) and SAD (sum of absolute differences). The temporal contribution of metrics to the video stream encoding process, in particular to the motion compensation process, is estimated. An algorithm combining classical SAD and parameter estimation based on characteristic points is proposed, which will reduce the metric calculation time for estimating block similarity. The reduction of SAD calculation time due to reduction of comparison points is estimated for three proposed comparison templates - HSAD (half SAD), TSAD (third SAD), DSAD (diagonal SAD). For the selected templates, the results of processing the test video sequence were compared with the results obtained during the processing of the video sequence by the classic SAD. The main attention was paid to the assessment of the following parameters: the relative difference SAD, the increase in the number of candidate blocks, the overlap of candidate blocks.

Learning multiple instance deep representation for objects tracking

Object tracking has been widely used in various intelligent systems, such as pedestrian tracking, autonomous vehicles. To solve the problem that appearance changes and occlusion may lead to poor tracking performance, we propose a multiple instance learning (MIL) based method for object tracking. To achieve this task, we first manually label the first several frames of video stream in image level, which can indicate that whether a target object in the video stream. Then, we leverage a pre-trained convolutional neural network that has rich prior information to extract deep representation of target object. Since the location of the same object in adjacent frames is similar, we introduce a particle filter to predict the location of target object within a specific region. Comprehensive experiments have shown the effectiveness of our proposed method.

METHOD OF TRANSFORMANT CODE DESCRIPTION CODING BASED ON INDEPENDENT CODE STRUCTURES IN THE TECHNOLOGY OF THE VIDEO-STREAM INTENSITY CONTROL

The article provides a general description of the approach to building a code representation of video stream fragments, namely, a DCT transformer. Such code representation is a set of codograms of individual bit planes of transformants. In turn, a codogram of each bit plane is constructed using the detection of the lengths of a series of binary elements within each of them. Detection of a series of binary elements within the proposed method is in the direction of the rows. The resulting bitmap codes are formed on the basis of non-equilibrium position codes. Unlike traditional coding techniques that aim at reducing probabilistic statistical redundancy, in this case, the structural redundancy of the bit description of video stream frame transforms is eliminated. In this case, the code description of each of the transformants is formed separately. In other words, within the code structures, the transformants of each of the codograms of its bit planes are independent of each other. That is, with the exception of one or more codograms that carry a description of one or another number of bit planes, no transformants occur on the receiving side of the destruction of the entire code description. That is, the generated code description allows you to repair the transformant on the receiving side with some errors. It turns out that such manipulation of the number of codograms that were initially used to describe the transform of the video stream frame allows to change the number of bits required for its code description. Thus, in this way, there is actually an additional reduction in the information intensity of the transmitted data, in addition to the reduction that is achieved by actually translating the transformant description to the appearance of the position code. In turn, this makes it possible to use the encoding method in question as a basis for video intensity management technology. The developed method does not require significant computing power for its effective functioning, since the construction of the bit description code requires less hardware resources by reducing the dynamic range of computation compared to component coding.

Deep Neural Network Method of Recognizing the Critical Situations for Transport Systems by Video Images

The deep neural network method of recognizing critical situations for transport systems according to video frames from the intelligent vehicles cameras is offered, that is effective in terms of accuracy and high-speed performance. Unlike the known solutions for the objects and normal or critical situations detection and recognition, it uses the classification with the subsequent reinforcement on the basis of several video stream frames and with the automatic annotation algorithm. The adapted architectures of neural networks are offered: the dual network to identify drivers and passengers according to the face image, the network with independent recurrent layers to classify situations according to the video fragment. The scheme of the intellectual distributed city system of transport safety using the cameras and on-board computers united in a single network is offered. Software modules in Python are developed and natural experiments are made. The possibility of the offered algorithms and programs in UGV or in the driver assistant systems implementation is shown with the illustrating examples in real-time.

КОДУВАННЯ БІТОВОГО ПРЕДСТАВЛЕННЯ ТРАНСФОРМАНТ У РАМКАХ УПРАВЛІННЯ БІТОВОЮ ШВИДКІСТЮ ВІДЕО

The conceptual basements of constructing an effective encoding method within the bit rate control module of video traffic in the video data processing system at the source level are considered. The essence of using the proposed method in the course of the video stream bit rate controlling disclosed, namely, the principles of constructing the fragment of the frame code representation and approaches for determining the structural units of the individual video frame within which the control is performed. The method focuses on processing the bit representation of the DCT transformants, and at this processing stage transformant was considered as a structural component of the video stream frame at which the encoding is performed. At the same time, to ensure the video traffic bit rate controlling flexibility, decomposition is performed with respect to each of the transformants to the level of the plurality of bit planes. It is argued that the proposed approach is potentially capable to reducing the video stream bit rate in the worst conditions, that is, when component coding is performed. In addition, this principle of video stream fragmen code representation forming allows to control the level of error that can be made in the bit rate control process. However, in conditions where the bit representation of the transformant is encoded, the method is able to provide higher compression rates as a result of the fact that the values ​​of the detection probability of binary series lengths and the values ​​of detected lengths within the bit plane will be greater than in the case of component coding. This is explained by the structural features of the distribution of binary elements within each of the bit planes, which together form the transformer DCT. In particular, high-frequency transformer regions are most often formed by chains of zero elements. The solutions proposed in the development of the encoding method are able to provide sufficient flexibility to control the bit rate of the video stream, as well as the ability to quickly change the bit rate in a wide range of values.