scholarly journals Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Sima Ahmadpour ◽  
Tat-Chee Wan ◽  
Zohreh Toghrayee ◽  
Fariba HematiGazafi
Keyword(s):  
Video Compression ◽  
High Performance ◽  
Hypothesis Test ◽  
Video Data ◽  
Video Codec ◽  
Video Frame ◽  
Scalable Video ◽  
Compression Technique ◽  
Data Set ◽  
Frame Size

Designing an effective and high performance network requires an accurate characterization and modeling of network traffic. The modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of its statistical properties and finds the best distribution using both the graphical method and the hypothesis test. The data set used in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three different movies.

An Improved Object-Based Video Segmentation Method

2012 ◽  
Vol 220-223 ◽  
pp. 2445-2449
Author(s):  
Wen Dan Xu ◽  
Xin Quan Lai ◽  
Dong Lai Xu
Keyword(s):  
Motion Estimation ◽  
Video Compression ◽  
High Performance ◽  
Video Segmentation ◽  
Error Function ◽  
Least Square ◽  
Video Frame ◽  
Merging Process ◽  
Initial Segmentation ◽  
Two Stages

This paper presents an improved video segmentation scheme, which consists of two stages: initial segmentation and motion estimation. In the initial segmentation, the watershed transformation followed by a region adjacency graph guided region merging process is used to partition the first video frame into spatial homogenous regions. Then the motion of changed region is estimated. Based on the highly efficient quadratic motion model, the motion estimation is undertaken using Gauss-Newton Levenberg-Marquardt method to minimize the least-square error function. Experimental results show the proposed scheme provides high performance in terms of segmentation accuracy and video compression ratio.

scholarly journals VIDEO COPY DETECTION UTILIZING THE LOG-POLAR TRANSFORMATION

2016 ◽  
pp. 8-13
Author(s):  
Daniel Reynolds ◽  
Richard A. Messner
Keyword(s):  
Data Reduction ◽  
Reduction Process ◽  
Video Data ◽  
Video Frame ◽  
Detection Accuracy ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Frame Size ◽  
Detection Process ◽  
Processing Step

Video copy detection is the process of comparing and analyzing videos to extract a measure of their similarity in order to determine if they are copies, modified versions, or completely different videos. With video frame sizes increasing rapidly, it is important to allow for a data reduction process to take place in order to achieve fast video comparisons. Further, detecting video streaming and storage of legal and illegal video data necessitates the fast and efficient implementation of video copy detection algorithms. In this paper some commonly used algorithms for video copy detection are implemented with the Log-Polar transformation being used as a pre-processing step to reduce the frame size prior to signature calculation. Two global based algorithms were chosen to validate the use of Log-Polar as an acceptable data reduction stage. The results of this research demonstrate that the addition of this pre-processing step significantly reduces the computation time of the overall video copy detection process while not significantly affecting the detection accuracy of the algorithm used for the detection process.

scholarly journals An experimental survey of no-reference video quality assessment methods

2016 ◽  
Vol 12 (1) ◽  
pp. 66-86 ◽  
Author(s):  
Maria Torres Vega ◽  
Vittorio Sguazzo ◽  
Decebal Constantin Mocanu ◽  
Antonio Liotta
Keyword(s):  
Video Compression ◽  
Video Quality ◽  
Video Data ◽  
Original Video ◽  
Data Set ◽  
Content Type ◽  
Quality Metric ◽  
Simulated Network ◽  
Video Quality Metric

Purpose The Video Quality Metric (VQM) is one of the most used objective methods to assess video quality, because of its high correlation with the human visual system (HVS). VQM is, however, not viable in real-time deployments such as mobile streaming, not only due to its high computational demands but also because, as a Full Reference (FR) metric, it requires both the original video and its impaired counterpart. In contrast, No Reference (NR) objective algorithms operate directly on the impaired video and are considerably faster but loose out in accuracy. The purpose of this paper is to study how differently NR metrics perform in the presence of network impairments. Design/methodology/approach The authors assess eight NR metrics, alongside a lightweight FR metric, using VQM as benchmark in a self-developed network-impaired video data set. This paper covers a range of methods, a diverse set of video types and encoding conditions and a variety of network impairment test-cases. Findings The authors show the extent by which packet loss affects different video types, correlating the accuracy of NR metrics to the FR benchmark. This paper helps identifying the conditions under which simple metrics may be used effectively and indicates an avenue to control the quality of streaming systems. Originality/value Most studies in literature have focused on assessing streams that are either unaffected by the network (e.g. looking at the effects of video compression algorithms) or are affected by synthetic network impairments (i.e. via simulated network conditions). The authors show that when streams are affected by real network conditions, assessing Quality of Experience becomes even harder, as the existing metrics perform poorly.

scholarly journals SECURE VIDEO CODED SYSTEM MODEL

2016 ◽  
Vol 42 (1) ◽  
pp. 5-9
Author(s):  
Tariq Fadil
Keyword(s):  
Video Compression ◽  
Orthogonal Frequency Division Multiplexing ◽  
Motion Vector ◽  
Video Signal ◽  
Video Data ◽  
Video Codec ◽  
System Model ◽  
Vector Data ◽  
Signal Sample ◽  
Performance Results

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results.

scholarly journals Real-Time Layered Video Compression using SIMD Computation

1998 ◽  
Vol 5 (45) ◽  
Author(s):  
Morten Vadskær Jensen ◽  
Brian Nielsen
Keyword(s):  
Real Time ◽  
Video Compression ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Video Stream ◽  
Video Codec ◽  
Discrete Cosine Transforms ◽  
Wide Range ◽  
Layered Video ◽  
The Difference

We present the design and implementation of a high performance layered video codec, designed for deployment in bandwidth heterogeneous networks. The codec combines wavelet based subband decomposition and discrete cosine transforms to facilitate layered spatial and SNR (signal-to-noise ratio) coding for bit-rate adaptation to a wide range of receiver capabilities. We show how a test video stream can be partitioned into several distinct layers of increasing visual quality and bandwidth requirements, with the difference between highest and lowest requirement being 47 : 1. Through the use of the Visual Instruction Set on SUN's Ultra-SPARC platform we demonstrate how SIMD parallel image processing enables real-time layered encoding and decoding in software. Our 384 * 320 * 24-bit test video stream is partitioned into 21 layers at a speed of 39 frames per second and reconstructed at 28 frames per second. Our VIS accelerated encoder stages are about 3-4 times as fast as an optimized C version. We find that this speed-up is well worth the extra implementation effort.

scholarly journals SelectStitch: Automated Frame Segmentation and Stitching to Create Composite Images from Otoscope Video Clips

2020 ◽  
Vol 10 (17) ◽  
pp. 5894
Author(s):  
Hamidullah Binol ◽  
Aaron C. Moberly ◽  
Muhammad Khalid Khan Niazi ◽  
Garth Essig ◽  
Jay Shah ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Video Data ◽  
Video Frame ◽  
Frame Size ◽  
Composite Image ◽  
Frame Selection ◽  
Segmentation Approach ◽  
Base Composite ◽  
Diagnostic Capabilities ◽  
Repeat Assessment

Background and Objective: the aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset. Methods: SelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities. Results: In the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2. Conclusions: We conclude that the frame selection and stitching will increase the probability of detecting a lesion even if it appears in a few frames.

scholarly journals On the inference speed and video-compression robustness of DeepLabCut

2018 ◽  
Author(s):  
Alexander Mathis ◽  
Richard Warren
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Video Compression ◽  
Pose Estimation ◽  
Efficient Method ◽  
Deep Neural Networks ◽  
Video Data ◽  
Frame Size ◽  
Highly Efficient

Pose estimation is crucial for many applications in neuroscience, biomechanics, genetics and beyond. We recently presented a highly efficient method for markerless pose estimation based on transfer learning with deep neural networks called DeepLabCut. Current experiments produce vast amounts of video data, which pose challenges for both storage and analysis. Here we improve the inference speed of DeepLabCut by up to tenfold and benchmark these updates on various CPUs and GPUs. In particular, depending on the frame size, poses can be inferred offline at up to 1200 frames per second (FPS). For instance, 278 × 278 images can be processed at 225 FPS on a GTX 1080 Ti graphics card. Furthermore, we show that DeepLabCut is highly robust to standard video compression (ffmpeg). Compression rates of greater than 1,000 only decrease accuracy by about half a pixel (for 640 × 480 frame size). DeepLabCut’s speed and robustness to compression can save both time and hardware expenses.

scholarly journals Novel algorithms for protective digital privacy

2019 ◽  
Vol 8 (3) ◽  
pp. 184
Author(s):  
Y. N. Prajapati ◽  
M. K. Srivastava
Keyword(s):  
Video Compression ◽  
Security Policy ◽  
Processing Time ◽  
Video Quality ◽  
Video Data ◽  
Digital Information ◽  
It Services ◽  
File Size ◽  
Compression Technique ◽  
Privacy Measures

Video is the recording, reproducing, or broadcasting of moving visual images. Visual multimedia source that combines a sequence of images to form a moving picture. The video transmits a signal to a screen and processes the order in which the screen captures should be shown. Videos usually have audio components that correspond with the pictures being shown on the screen. Video compression technologies are about reducing and removing redundant video data so that a digital video file can be effectively sent over a network and stored on computer disks. With efficient compression techniques, a significant reduction in file size can be achieved with little or no adverse effect on the visual quality. The video quality, however, can be affected if the file size is further lowered by raising the compression level for a given compression technique. Security is about the protection of assets. Security, in information technology <a href="http://searchdatacenter.techtarget.com/definition/IT">(IT), </a>is the defense of digital information and IT assets against internal and external, malicious and accidental threats. This defense includes detection, prevention and response to threats through the use of <a href="http://searchsecurity.techtarget.com/definition/security-policy">security policies, </a>software tools and IT services. Security refers to protective digital privacy measures that are applied to prevent unauthorized access to computers, databases and websites. Cryptography is closely related to the disciplines of <a href="http://searchsecurity.techtarget.com/definition/cryptology">cryptology </a>and <a href="http://searchsecurity.techtarget.com/definition/cryptanalysis">cryptanalysis. </a>Cryptography includes techniques such as microdots, merging words with images, and other ways to hide information in storage or transit. However, in today's computer-centric world, cryptography is most often associated with scrambling <a href="http://searchsecurity.techtarget.com/definition/plaintext">plaintext </a>(ordinary text, sometimes referred to as clear text into <a href="http://searchcio-midmarket.techtarget.com/definition/ciphertext">cipher text </a>(a process called <a href="http://searchsecurity.techtarget.com/definition/encryption">encryption), </a>then back again (known as decryption). Cryptography is evergreen and developments. Cryptography protects users by providing functionality for the encryption of data and authentication of other users. Compression is the process of reducing the number of bits or bytes needed to represent a given set of data. It allows saving more data. The project aims to implement security algorithm for data security. The data will be first encrypted using security techniques and that are done at the same time then it takes less processing time and more speed compression techniques will applied. If encryption and compression are done at the same time then it takes less processing time and more speed.

scholarly journals Efficient and Low Complexity Surveillance Video Compression using Distributed Scalable Video Coding

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Le Dao Thi Hue ◽  
Luong Pham Van ◽  
Duong Dinh Trieu ◽  
Xiem HoangVan
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
Video Surveillance ◽  
High Efficiency ◽  
Scalable Video Coding ◽  
Low Complexity ◽  
Video Data ◽  
Base Layer ◽  
Scalable Video ◽  
Surveillance Video

Video surveillance has been playing an important role in public safety and privacy protection in recent years thanks to its capability of providing the activity monitoring and content analyzing. However, the data associated with long hours surveillance video is huge, making it less attractive to practical applications. In this paper, we propose a low complexity, yet efficient scalable video coding solution for video surveillance system. The proposed surveillance video compression scheme is able to provide the quality scalability feature by following a layered coding structure that consists of one or several enhancement layers on the top of a base layer. In addition, to maintain the backward compatibility with the current video coding standards, the state-of-the-art video coding standard, i.e., High Efficiency Video Coding (HEVC), is employed in the proposed coding solution to compress the base layer. To satisfy the low complexity requirement of the encoder for the video surveillance systems, the distributed coding concept is employed at the enhancement layers. Experiments conducted for a rich set of surveillance video data shown that the proposed surveillance - distributed scalable video coding (S-DSVC) solution significantly outperforms relevant video coding benchmarks, notably the SHVC standard and the HEVC-simulcasting while requiring much lower computational complexity at the encoder which is essential for practical video surveillance applications.

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