Streaming of high-quality video contents as part of multimedia communication, has become essential nowadays. Video delivered over the network suffers from different kind of impairments, which degrades its quality. Such network impairments effect differs among different types of codec used. This paper present the effects of network degradation factors such as packet loss and jitter over H.264 and H.265 encoded video sequences. In addition to the codec used, we also focused on the different level of temporal and spatial aspect within the videos. Among different basic test methods, double stimulus impairments scale was used to complete the experiment as subjective measures of assessment metric from user’s perspective.The result illustrates that differently encoded video sequences react differently to the network impairments and are very sensitive to a transmission error. Similarly, it also shows that user’s experience is affected according to the motion level of video.
Comparison of Cinepak, Intel, Microsoft Video and Indeo Codec for Video Compr...ijma
The file size and picture quality are factors to be considered for streaming, storage and transmitting videos
over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video
compression. The peak signal to noise ratio is used to compare the quality of such video compressed using
AVI codecs. The most widely used objective measurement by developers of video processing systems is
Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and
depends on the mean squared error (MSE) between an original and an impaired image or video, relative to
(2n-1)2.
Previous research done regarding assessing of video quality has been mainly by the use of subjective
methods, and there is still no standard method for objective assessments. Although it has been considered
that compression might not be significant in future as storage and transmission capabilities improve, but at
low bandwidths compression makes communication possible.
An Overview on Multimedia Transcoding Techniques on Streaming Digital Contentsidescitation
The current IT infrastructure as well as various
commercial applications are directly formulated based on
deployment in multimedia system e.g. education, marketing,
risk management, tele-medicines, military etc. One of the
challenges found in using such application is to deliver
uninterrupted stream of video between multiple terminals
e.g. smart-phone, PDAs, laptops, IPTV etc. The research shows
that there is a stipulated need of designing novel mechanism
of bit rate adjustment as well as format conversion policy so
that the source stream may stream well in diverse end devices
with multiple configuration of processor, memory, decoding
etc. This paper discusses various eminent points from
literature that will throw better highlights in understanding
a schema of direct digital-to-digital data conversion of one
encoding to another termed as transcoding. Although
multimedia transcoding has covered more than a decade in
the area of research, but unfortunately, there is a huge trade-
off between the application, service, resource constraint, and
hardware design that gives rise to QoS issues.
Over recent years there has been a considerable shift, from quality of service (QoS) to quality of experience (QoE), when evaluating video delivery across networks. Hence, we first explore the need for this shift towards user-QoE in the video delivery ecosystem. Further, we investigate major QoE metrics researchers use in the evaluation of DASH users. We point out a huge problem with DASH beginning with its transport layer protocol. DASH utilizes Transmission control protocol (TCP) as the transport layer protocol. Thus, we give an overview of the mechanism of Transmission Control Protocol (TCP) and two mechanisms greatly impacting the streaming process: (1) TCP
congestion mechanism and (2) TCP Fast Start. This leads us to investigate the impact of these TCP mechanisms on DASH players and consequently user-QoE.
The document discusses legacy connectivity and protocols. It describes legacy integration as integrating J2EE components with legacy systems. The key approaches to legacy integration are data level integration, application interface integration, method level integration, and user interface level integration. Legacy connectivity can be achieved using Java Native Interface (JNI), J2EE Connector Architecture, and web services. JNI allows Java code to call native methods written in other languages like C/C++. The J2EE Connector Architecture standardizes connectivity through resource adapters. Web services provide a platform-independent approach through XML protocols.
The document discusses messaging and internationalization. It covers messaging using Java Message Service (JMS), including the need for messaging, messaging architecture, types of messaging, messaging models, messaging servers, components of a JMS application, developing effective messaging solutions, and implementing JMS. It also discusses internationalizing J2EE applications.
This document discusses EJB technology and provides summaries of key concepts:
1. It defines the EJB container model and describes features like security, distributed access, and lifecycle management.
2. It compares the lifecycles of stateless session beans, stateful session beans, entity beans, and message-driven beans.
3. It contrasts stateful and stateless session beans and discusses differences in client state, pooling, lifecycles, and more. It also compares session beans and entity beans in terms of representing processes versus data.
The document discusses Java 2 Enterprise Edition (J2EE) application security. It covers security threat assessment, the Java 2 security model, and Java security APIs. The Java 2 security model provides access controls and allows downloading and running applications securely. It uses techniques like cryptography, digital signatures, and SSL. The Java Cryptography Extensions API provides methods for encrypting data, generating keys, and authentication.
PERFORMANCE EVALUATION OF H.265/MPEG-HEVC, VP9 AND H.264/MPEGAVC VIDEO CODINGijma
This study evaluates the performance of the three latest video codecs H.265/MPEG-HEVC, H.264/MPEGAVC
and VP9. The evaluation is based on both subjective and objective quality metrics. The assessment
metric Double Stimulus Impairment Scale (DSIS) is used to evaluate the subjective quality of the
compressed video sequences. The Peak Signal-to-Noise Ratio (PSNR) metricis used for the objective
evaluation. Moreover, this work studies the effect of frame rate and resolution on the encoders’
performance. The extensive number of experiments are conducted with similar encoding configurations for
the three studied encoders. The evaluation results show that H.265/MPEG-HEVC provides superior bitrate
saving capabilities compared to H.264 and VP9. However, VP9 shows lower encoding time than
H.265/MPEG-HEVC but higher encoding time compared to H.264.
Comparison of Cinepak, Intel, Microsoft Video and Indeo Codec for Video Compr...ijma
The file size and picture quality are factors to be considered for streaming, storage and transmitting videos
over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video
compression. The peak signal to noise ratio is used to compare the quality of such video compressed using
AVI codecs. The most widely used objective measurement by developers of video processing systems is
Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and
depends on the mean squared error (MSE) between an original and an impaired image or video, relative to
(2n-1)2.
Previous research done regarding assessing of video quality has been mainly by the use of subjective
methods, and there is still no standard method for objective assessments. Although it has been considered
that compression might not be significant in future as storage and transmission capabilities improve, but at
low bandwidths compression makes communication possible.
An Overview on Multimedia Transcoding Techniques on Streaming Digital Contentsidescitation
The current IT infrastructure as well as various
commercial applications are directly formulated based on
deployment in multimedia system e.g. education, marketing,
risk management, tele-medicines, military etc. One of the
challenges found in using such application is to deliver
uninterrupted stream of video between multiple terminals
e.g. smart-phone, PDAs, laptops, IPTV etc. The research shows
that there is a stipulated need of designing novel mechanism
of bit rate adjustment as well as format conversion policy so
that the source stream may stream well in diverse end devices
with multiple configuration of processor, memory, decoding
etc. This paper discusses various eminent points from
literature that will throw better highlights in understanding
a schema of direct digital-to-digital data conversion of one
encoding to another termed as transcoding. Although
multimedia transcoding has covered more than a decade in
the area of research, but unfortunately, there is a huge trade-
off between the application, service, resource constraint, and
hardware design that gives rise to QoS issues.
Over recent years there has been a considerable shift, from quality of service (QoS) to quality of experience (QoE), when evaluating video delivery across networks. Hence, we first explore the need for this shift towards user-QoE in the video delivery ecosystem. Further, we investigate major QoE metrics researchers use in the evaluation of DASH users. We point out a huge problem with DASH beginning with its transport layer protocol. DASH utilizes Transmission control protocol (TCP) as the transport layer protocol. Thus, we give an overview of the mechanism of Transmission Control Protocol (TCP) and two mechanisms greatly impacting the streaming process: (1) TCP
congestion mechanism and (2) TCP Fast Start. This leads us to investigate the impact of these TCP mechanisms on DASH players and consequently user-QoE.
The document discusses legacy connectivity and protocols. It describes legacy integration as integrating J2EE components with legacy systems. The key approaches to legacy integration are data level integration, application interface integration, method level integration, and user interface level integration. Legacy connectivity can be achieved using Java Native Interface (JNI), J2EE Connector Architecture, and web services. JNI allows Java code to call native methods written in other languages like C/C++. The J2EE Connector Architecture standardizes connectivity through resource adapters. Web services provide a platform-independent approach through XML protocols.
The document discusses messaging and internationalization. It covers messaging using Java Message Service (JMS), including the need for messaging, messaging architecture, types of messaging, messaging models, messaging servers, components of a JMS application, developing effective messaging solutions, and implementing JMS. It also discusses internationalizing J2EE applications.
This document discusses EJB technology and provides summaries of key concepts:
1. It defines the EJB container model and describes features like security, distributed access, and lifecycle management.
2. It compares the lifecycles of stateless session beans, stateful session beans, entity beans, and message-driven beans.
3. It contrasts stateful and stateless session beans and discusses differences in client state, pooling, lifecycles, and more. It also compares session beans and entity beans in terms of representing processes versus data.
The document discusses Java 2 Enterprise Edition (J2EE) application security. It covers security threat assessment, the Java 2 security model, and Java security APIs. The Java 2 security model provides access controls and allows downloading and running applications securely. It uses techniques like cryptography, digital signatures, and SSL. The Java Cryptography Extensions API provides methods for encrypting data, generating keys, and authentication.
PERFORMANCE EVALUATION OF H.265/MPEG-HEVC, VP9 AND H.264/MPEGAVC VIDEO CODINGijma
This study evaluates the performance of the three latest video codecs H.265/MPEG-HEVC, H.264/MPEGAVC
and VP9. The evaluation is based on both subjective and objective quality metrics. The assessment
metric Double Stimulus Impairment Scale (DSIS) is used to evaluate the subjective quality of the
compressed video sequences. The Peak Signal-to-Noise Ratio (PSNR) metricis used for the objective
evaluation. Moreover, this work studies the effect of frame rate and resolution on the encoders’
performance. The extensive number of experiments are conducted with similar encoding configurations for
the three studied encoders. The evaluation results show that H.265/MPEG-HEVC provides superior bitrate
saving capabilities compared to H.264 and VP9. However, VP9 shows lower encoding time than
H.265/MPEG-HEVC but higher encoding time compared to H.264.
High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.
This document discusses video codec delay sources and magnitudes. It begins by outlining common delay sources in video transmission systems, including processing, packetization, transmission, and other network delays. It then examines specific delays introduced by common video codecs, calculating the delay for various codecs in two scenarios: single frame per packet and multiple frames per packet. The document finds that codecs designed for high quality introduce higher delays, while decoder delay is typically about half the encoder delay. Overall, it provides guidance on selecting video codecs based on their delay characteristics for different applications.
PERCEPTUALLY LOSSLESS COMPRESSION WITH ERROR CONCEALMENT FOR PERISCOPE AND SO...sipij
We present a video compression framework that has two key features. First, we aim at achieving
perceptually lossless compression for low frame rate videos (6 fps). Four well-known video codecs in the
literature have been evaluated and the performance was assessed using four well-known performance
metrics. Second, we investigated the impact of error concealment algorithms for handling corrupted pixels
due to transmission errors in communication channels. Extensive experiments using actual videos have
been performed to demonstrate the proposed framework.
Optimal Streaming Protocol for VoD Using Clients' Residual BandwidthIDES Editor
A true VoD system has tremendous demand in the
market. The existing VoD system does not cater the needs
and demands of the market. The major problem in the VoD
system is serving of clients with expected QoS is difficult. In
this paper, we proposed a protocol and algorithm that
chains the proxy servers and subscribed clients. Our
objective is to send one server stream and this stream should
be served to N asynchronous clients. The server bandwidth
is scarcity and on the client uplink bandwidth is
underutilized. In this protocol, we are using client’s residual
bandwidth such that the load on the server bandwidth is
reduced. We have proved that optimal utilization of the
buffer and bandwidth for the entire VoD system and also
less rejection ratio of the clients.
This paper develops neural network models that can predict user quality of experience (QoE) for Internet Protocol television (IPTV) applications in real time based on network measurements. The models account for multiple video resolutions, audio/video codecs, and network conditions including jitter, packet loss, and router queuing disciplines. The models were trained using data from objective network simulations and subjective human experiments evaluating mean opinion scores for quality. Evaluation shows the models accurately and quickly predict user QoE for IPTV under different conditions and can be used to monitor network quality in real-time.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document summarizes two video watermarking algorithms that use Singular Value Decomposition (SVD). The first algorithm embeds watermark bits diagonally in the SVD-transformed U, S, or V matrices of video frames. The second algorithm embeds bits in blocks of the U or V matrices. Both algorithms were evaluated based on imperceptibility, robustness, and data payload. The diagonal embedding achieved better robustness while the block-wise embedding had a higher data payload rate. SVD transforms video frames, distributing the watermark across spatial and frequency domains for improved imperceptibility and robustness against attacks.
Siva Srinivas is a Senior Technical Specialist at TPVision in Bangalore, India. He has over 9 years of experience developing Android and embedded software solutions. His skills include Java, C/C++, Android, REST APIs, DVB standards, and Agile methodologies. He has experience leading projects for digital TV applications and set-top boxes.
TUD at MediaEval 2012 genre tagging task: Multi-modality video categorization...MediaEval2012
This document summarizes two projects presented by Delft University of Technology at the TUD MediaEval 2012 Tagging Task. The first project used one-vs-all classifiers and feature fusion to perform multi-modality video categorization. The second project compared different models for predicting tags based on automatic speech recognition output, including support vector machines, dynamic Bayesian networks, and conditional random fields. The dynamic Bayesian network model achieved the best performance overall.
Opportunistic and playback sensitive scheduling for video streamingijwmn
Given the strict Quality of Service (QoS) requirements of video streaming, this paper proposes a novel
solution for simultaneous streaming of multiple video sessions over a mobile cellular system. The proposed
solution combines a buffer management strategy with a packet scheduling algorithm. The buffer
management strategy selectively discards packets of a user from base station buffer whereas the packet
scheduling algorithm schedules packets of a user according to its instantaneous channel quality, average
throughput and playback buffer information. Simulation results demonstrate that the proposed solution is
effective in providing a continuous video playback with good perceptual quality for more users. If at least a
good perceptual quality is to be satisfied for all users (QoS constraint of video streaming), then the
proposed solution improves the system capacity by 40% over a conventional packet scheduling algorithm.
This paper proposes a blind quality algorithm to analyze streaming video content in 5G networks. The algorithm detects common streaming errors like color degradation, frozen frames, and packet loss. It is included in a "Quality Probe" application that operates as a virtual network function and sends quality reports. The algorithm was tested on sequences with different impairments from a video quality database. It successfully detected packet loss, color errors, and frozen frames. The results validate the algorithm and show the need for intelligent network nodes to monitor quality and adapt transmissions to improve users' experience in 5G networks. Future work includes additional metrics, processing time analysis, and testing in a real network.
Beng Chuan Lim has over 13 years of experience in security consultancy and project management. He holds a Honors Degree in Engineering (Electrical) with a major in wireless communications. Currently he is a Project Manager at SP PowerGrid Ltd, where he is responsible for designing, planning, and managing security system upgrade projects to protect company assets. Previously he held management positions at Changi Airport Group and Certis CISCO Consulting Services, where he led security system implementation projects for various government and commercial clients in Singapore, Southeast Asia, and the Middle East. He has strong technical skills in security engineering and solutions design, as well as project management experience delivering diverse security assignments in compliance with industry standards.
COMPARISON OF CINEPAK, INTEL, MICROSOFT VIDEO AND INDEO CODEC FOR VIDEO COMPR...ijma
The file size and picture quality are factors to be considered for streaming, storage and transmitting videos
over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video
compression. The peak signal to noise ratio is used to compare the quality of such video compressed using
AVI codecs. The most widely used objective measurement by developers of video processing systems is
Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and
depends on the mean squared error (MSE) between an original and an impaired image or video, relative to
(2n-1)2.
COMPARISON OF CINEPAK, INTEL, MICROSOFT VIDEO AND INDEO CODEC FOR VIDEO COMPR...ijma
This document compares four video codecs - Cinepak, Intel Indeo, Microsoft Video, and Indeo Video - by measuring the peak signal-to-noise ratio (PSNR) of videos compressed with each codec. The document provides background on video compression standards and objective video quality measurement. It describes conducting an experiment where several video clips were compressed using the four codecs and their PSNR values calculated relative to the original uncompressed videos. PSNR was chosen as the quality metric since it can be easily calculated and produces repeatable results. The compressed videos were in AVI format to be analyzed by video quality assessment software.
COMPARISON OF CINEPAK, INTEL, MICROSOFT VIDEO AND INDEO CODEC FOR VIDEO COMPR...ijma
The file size and picture quality are factors to be considered for streaming, storage and transmitting videos
over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video
compression. The peak signal to noise ratio is used to compare the quality of such video compressed using
AVI codecs. The most widely used objective measurement by developers of video processing systems is
Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and
depends on the mean squared error (MSE) between an original and an impaired image or video, relative to
(2n-1)2.
Previous research done regarding assessing of video quality has been mainly by the use of subjective
methods, and there is still no standard method for objective assessments. Although it has been considered
that compression might not be significant in future as storage and transmission capabilities improve, but at
low bandwidths compression makes communication possible
The impact of jitter on the HEVC video streaming with Multiple CodingHakimSahour
This document discusses the impact of jitter on video quality when streaming HEVC encoded video over wireless networks. It presents a study evaluating the effects of quantization parameter (QP) values, video content, and jitter on quality of experience (QoE). The study finds that using higher QP values, which lowers bitrate and increases compression, degrades video quality as measured by PSNR. It also finds that different video content results in varying PSNR values for the same encoding settings. Additionally, the results show that adjusting the QP value can help recover from the negative effects of jitter on received video quality. The document proposes using multiple description coding (MDC) to further improve transmission over error-prone wireless channels.
Comparative Study of Compression Techniques for Synthetic Videosijma
We evaluate the performance of three state of the art video codecs on synthetic videos. The evaluation is
based on both subjective and objective quality metrics. The subjective quality of the compressed video
sequences is evaluated using the Double Stimulus Impairment Scale (DSIS) assessment metric while the
Peak Signal-to-Noise Ratio (PSNR) is used for the objective evaluation. An extensive number of
experiments are conducted to study the effect of frame rate and resolution on codecs’ performance for
synthetic videos. The evaluation results show that video codecs respond in different ways to frame rate and
frame resolution change. H.264 shows superior capabilities compared to other codecs. Mean Opinion
Score (MOS) results are shown for various bitrates, frame rates and frame resolutions.
Comparative study of compression techniques for synthetic videosijma
We evaluate the performance of three state of the art video codecs on synthetic videos. The evaluation is
based on both subjective and objective quality metrics. The subjective quality of the compressed video
sequences is evaluated using the Double Stimulus Impairment Scale (DSIS) assessment metric while the
Peak Signal-to-Noise Ratio (PSNR) is used for the objective evaluation. An extensive number of
experiments are conducted to study the effect of frame rate and resolution on codecs’ performance for
synthetic videos. The evaluation results show that video codecs respond in different ways to frame rate and
frame resolution change. H.264 shows superior capabilities compared to other codecs. Mean Opinion
Score (MOS) results are shown for various bitrates, frame rates and frame resolutions.
A survey on Measurement of Objective Video Quality in Social Cloud using Mach...IRJET Journal
This document discusses using machine learning techniques for objective measurement of video quality in social cloud applications. It proposes a method using convolutional neural networks (CNNs) trained on a large dataset of videos evaluated by human observers to predict video quality. The CNN model extracts quality-related features from videos which are used to train a machine learning model. The model achieved high accuracy in predicting subjective quality outcomes for new videos. Machine learning provides an objective way to assess video quality that can benefit users and service providers by optimizing video streaming quality. The document reviews key concepts like video quality assessment, deep learning and CNNs, and the typical methodology used in machine learning-based video quality measurement.
Analyzing Video Streaming Quality by Using Various Error Correction Methods o...IJERA Editor
Transmission video over ad hoc networks has become one of the most important and interesting subjects of study for researchers and programmers because of the strong relationship between video applications and frequent users of various mobile devices, such as laptops, PDAs, and mobile phones in all aspects of life. However, many challenges, such as packet loss, congestion (i.e., impairments at the network layer), multipath fading (i.e., impairments at the physical layer) [1], and link failure, exist in transferring video over ad hoc networks; these challenges negatively affect the quality of the perceived video [2].This study has investigated video transfer over ad hoc networks. The main challenges of transferring video over ad hoc networks as well as types of errors that may occur during video transmission, various types of video mechanisms, error correction methods, and different Quality of Service (QoS) parameters that affect the quality of the received video are also investigated.
Decision Making Analysis of Video Streaming Algorithm for Private Cloud Compu...IJECEIAES
The issue on how to effectively deliver video streaming contents over cloud computing infrastructures is tackled in this study. Basically, quality of service of video streaming is strongly influenced by bandwidth, jitter and data loss problems. A number of intelligent video streaming algorithms are proposed by using different techniques to deal with such issues. This study aims to propose and demonstrate a novel decision making analysis which combines ISO 9126 (international standard for software engineering) and Analytic Hierarchy Process to help experts selecting the best video streaming algorithm for the case of private cloud computing infrastructure. The given case study concluded that Scalable Streaming algorithm is the best algorithm to be implemented for delivering high quality of service of video streaming over the private cloud computing infrastructure.
An efficient modified video compression HEVC technique based on high quality assessment saliency features presented for the assessment of high quality videos. To create an efficient saliency map we extract global temporal alignment component and robust spatial components. To obtain high quality saliency here, we combine spatial saliency features and temporal saliency features together for different macroblocks in association with transformed residuals. In this way, our saliency model outperforms all the existing techniques. In this paper, we have generated high reconstruction quality video after compression considering SFU dataset. Our experimental result outperforms all the existing techniques in terms of saliency map detection, PSNR and high-resolution quality.
An efficient modified video compression HEVC technique based on high quality assessment saliency features presented for the assessment of high quality videos. To create an efficient saliency map we extract global temporal alignment component and robust spatial components. To obtain high quality saliency here, we combine spatial saliency features and temporal saliency features together for different macroblocks in association with transformed residuals. In this way, our saliency model outperforms all the existing techniques. In this paper, we have generated high reconstruction quality video after compression considering SFU dataset. Our experimental result outperforms all the existing techniques in terms of saliency map detection, PSNR and high-resolution quality.
High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.
This document discusses video codec delay sources and magnitudes. It begins by outlining common delay sources in video transmission systems, including processing, packetization, transmission, and other network delays. It then examines specific delays introduced by common video codecs, calculating the delay for various codecs in two scenarios: single frame per packet and multiple frames per packet. The document finds that codecs designed for high quality introduce higher delays, while decoder delay is typically about half the encoder delay. Overall, it provides guidance on selecting video codecs based on their delay characteristics for different applications.
PERCEPTUALLY LOSSLESS COMPRESSION WITH ERROR CONCEALMENT FOR PERISCOPE AND SO...sipij
We present a video compression framework that has two key features. First, we aim at achieving
perceptually lossless compression for low frame rate videos (6 fps). Four well-known video codecs in the
literature have been evaluated and the performance was assessed using four well-known performance
metrics. Second, we investigated the impact of error concealment algorithms for handling corrupted pixels
due to transmission errors in communication channels. Extensive experiments using actual videos have
been performed to demonstrate the proposed framework.
Optimal Streaming Protocol for VoD Using Clients' Residual BandwidthIDES Editor
A true VoD system has tremendous demand in the
market. The existing VoD system does not cater the needs
and demands of the market. The major problem in the VoD
system is serving of clients with expected QoS is difficult. In
this paper, we proposed a protocol and algorithm that
chains the proxy servers and subscribed clients. Our
objective is to send one server stream and this stream should
be served to N asynchronous clients. The server bandwidth
is scarcity and on the client uplink bandwidth is
underutilized. In this protocol, we are using client’s residual
bandwidth such that the load on the server bandwidth is
reduced. We have proved that optimal utilization of the
buffer and bandwidth for the entire VoD system and also
less rejection ratio of the clients.
This paper develops neural network models that can predict user quality of experience (QoE) for Internet Protocol television (IPTV) applications in real time based on network measurements. The models account for multiple video resolutions, audio/video codecs, and network conditions including jitter, packet loss, and router queuing disciplines. The models were trained using data from objective network simulations and subjective human experiments evaluating mean opinion scores for quality. Evaluation shows the models accurately and quickly predict user QoE for IPTV under different conditions and can be used to monitor network quality in real-time.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document summarizes two video watermarking algorithms that use Singular Value Decomposition (SVD). The first algorithm embeds watermark bits diagonally in the SVD-transformed U, S, or V matrices of video frames. The second algorithm embeds bits in blocks of the U or V matrices. Both algorithms were evaluated based on imperceptibility, robustness, and data payload. The diagonal embedding achieved better robustness while the block-wise embedding had a higher data payload rate. SVD transforms video frames, distributing the watermark across spatial and frequency domains for improved imperceptibility and robustness against attacks.
Siva Srinivas is a Senior Technical Specialist at TPVision in Bangalore, India. He has over 9 years of experience developing Android and embedded software solutions. His skills include Java, C/C++, Android, REST APIs, DVB standards, and Agile methodologies. He has experience leading projects for digital TV applications and set-top boxes.
TUD at MediaEval 2012 genre tagging task: Multi-modality video categorization...MediaEval2012
This document summarizes two projects presented by Delft University of Technology at the TUD MediaEval 2012 Tagging Task. The first project used one-vs-all classifiers and feature fusion to perform multi-modality video categorization. The second project compared different models for predicting tags based on automatic speech recognition output, including support vector machines, dynamic Bayesian networks, and conditional random fields. The dynamic Bayesian network model achieved the best performance overall.
Opportunistic and playback sensitive scheduling for video streamingijwmn
Given the strict Quality of Service (QoS) requirements of video streaming, this paper proposes a novel
solution for simultaneous streaming of multiple video sessions over a mobile cellular system. The proposed
solution combines a buffer management strategy with a packet scheduling algorithm. The buffer
management strategy selectively discards packets of a user from base station buffer whereas the packet
scheduling algorithm schedules packets of a user according to its instantaneous channel quality, average
throughput and playback buffer information. Simulation results demonstrate that the proposed solution is
effective in providing a continuous video playback with good perceptual quality for more users. If at least a
good perceptual quality is to be satisfied for all users (QoS constraint of video streaming), then the
proposed solution improves the system capacity by 40% over a conventional packet scheduling algorithm.
This paper proposes a blind quality algorithm to analyze streaming video content in 5G networks. The algorithm detects common streaming errors like color degradation, frozen frames, and packet loss. It is included in a "Quality Probe" application that operates as a virtual network function and sends quality reports. The algorithm was tested on sequences with different impairments from a video quality database. It successfully detected packet loss, color errors, and frozen frames. The results validate the algorithm and show the need for intelligent network nodes to monitor quality and adapt transmissions to improve users' experience in 5G networks. Future work includes additional metrics, processing time analysis, and testing in a real network.
Beng Chuan Lim has over 13 years of experience in security consultancy and project management. He holds a Honors Degree in Engineering (Electrical) with a major in wireless communications. Currently he is a Project Manager at SP PowerGrid Ltd, where he is responsible for designing, planning, and managing security system upgrade projects to protect company assets. Previously he held management positions at Changi Airport Group and Certis CISCO Consulting Services, where he led security system implementation projects for various government and commercial clients in Singapore, Southeast Asia, and the Middle East. He has strong technical skills in security engineering and solutions design, as well as project management experience delivering diverse security assignments in compliance with industry standards.
COMPARISON OF CINEPAK, INTEL, MICROSOFT VIDEO AND INDEO CODEC FOR VIDEO COMPR...ijma
The file size and picture quality are factors to be considered for streaming, storage and transmitting videos
over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video
compression. The peak signal to noise ratio is used to compare the quality of such video compressed using
AVI codecs. The most widely used objective measurement by developers of video processing systems is
Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and
depends on the mean squared error (MSE) between an original and an impaired image or video, relative to
(2n-1)2.
COMPARISON OF CINEPAK, INTEL, MICROSOFT VIDEO AND INDEO CODEC FOR VIDEO COMPR...ijma
This document compares four video codecs - Cinepak, Intel Indeo, Microsoft Video, and Indeo Video - by measuring the peak signal-to-noise ratio (PSNR) of videos compressed with each codec. The document provides background on video compression standards and objective video quality measurement. It describes conducting an experiment where several video clips were compressed using the four codecs and their PSNR values calculated relative to the original uncompressed videos. PSNR was chosen as the quality metric since it can be easily calculated and produces repeatable results. The compressed videos were in AVI format to be analyzed by video quality assessment software.
COMPARISON OF CINEPAK, INTEL, MICROSOFT VIDEO AND INDEO CODEC FOR VIDEO COMPR...ijma
The file size and picture quality are factors to be considered for streaming, storage and transmitting videos
over networks. This work compares Cinepak, Intel, Microsoft Video and Indeo Codec for video
compression. The peak signal to noise ratio is used to compare the quality of such video compressed using
AVI codecs. The most widely used objective measurement by developers of video processing systems is
Peak Signal-to-Noise Ratio (PSNR). Peak Signal to Noise Ration is measured on a logarithmic scale and
depends on the mean squared error (MSE) between an original and an impaired image or video, relative to
(2n-1)2.
Previous research done regarding assessing of video quality has been mainly by the use of subjective
methods, and there is still no standard method for objective assessments. Although it has been considered
that compression might not be significant in future as storage and transmission capabilities improve, but at
low bandwidths compression makes communication possible
The impact of jitter on the HEVC video streaming with Multiple CodingHakimSahour
This document discusses the impact of jitter on video quality when streaming HEVC encoded video over wireless networks. It presents a study evaluating the effects of quantization parameter (QP) values, video content, and jitter on quality of experience (QoE). The study finds that using higher QP values, which lowers bitrate and increases compression, degrades video quality as measured by PSNR. It also finds that different video content results in varying PSNR values for the same encoding settings. Additionally, the results show that adjusting the QP value can help recover from the negative effects of jitter on received video quality. The document proposes using multiple description coding (MDC) to further improve transmission over error-prone wireless channels.
Comparative Study of Compression Techniques for Synthetic Videosijma
We evaluate the performance of three state of the art video codecs on synthetic videos. The evaluation is
based on both subjective and objective quality metrics. The subjective quality of the compressed video
sequences is evaluated using the Double Stimulus Impairment Scale (DSIS) assessment metric while the
Peak Signal-to-Noise Ratio (PSNR) is used for the objective evaluation. An extensive number of
experiments are conducted to study the effect of frame rate and resolution on codecs’ performance for
synthetic videos. The evaluation results show that video codecs respond in different ways to frame rate and
frame resolution change. H.264 shows superior capabilities compared to other codecs. Mean Opinion
Score (MOS) results are shown for various bitrates, frame rates and frame resolutions.
Comparative study of compression techniques for synthetic videosijma
We evaluate the performance of three state of the art video codecs on synthetic videos. The evaluation is
based on both subjective and objective quality metrics. The subjective quality of the compressed video
sequences is evaluated using the Double Stimulus Impairment Scale (DSIS) assessment metric while the
Peak Signal-to-Noise Ratio (PSNR) is used for the objective evaluation. An extensive number of
experiments are conducted to study the effect of frame rate and resolution on codecs’ performance for
synthetic videos. The evaluation results show that video codecs respond in different ways to frame rate and
frame resolution change. H.264 shows superior capabilities compared to other codecs. Mean Opinion
Score (MOS) results are shown for various bitrates, frame rates and frame resolutions.
A survey on Measurement of Objective Video Quality in Social Cloud using Mach...IRJET Journal
This document discusses using machine learning techniques for objective measurement of video quality in social cloud applications. It proposes a method using convolutional neural networks (CNNs) trained on a large dataset of videos evaluated by human observers to predict video quality. The CNN model extracts quality-related features from videos which are used to train a machine learning model. The model achieved high accuracy in predicting subjective quality outcomes for new videos. Machine learning provides an objective way to assess video quality that can benefit users and service providers by optimizing video streaming quality. The document reviews key concepts like video quality assessment, deep learning and CNNs, and the typical methodology used in machine learning-based video quality measurement.
Analyzing Video Streaming Quality by Using Various Error Correction Methods o...IJERA Editor
Transmission video over ad hoc networks has become one of the most important and interesting subjects of study for researchers and programmers because of the strong relationship between video applications and frequent users of various mobile devices, such as laptops, PDAs, and mobile phones in all aspects of life. However, many challenges, such as packet loss, congestion (i.e., impairments at the network layer), multipath fading (i.e., impairments at the physical layer) [1], and link failure, exist in transferring video over ad hoc networks; these challenges negatively affect the quality of the perceived video [2].This study has investigated video transfer over ad hoc networks. The main challenges of transferring video over ad hoc networks as well as types of errors that may occur during video transmission, various types of video mechanisms, error correction methods, and different Quality of Service (QoS) parameters that affect the quality of the received video are also investigated.
Decision Making Analysis of Video Streaming Algorithm for Private Cloud Compu...IJECEIAES
The issue on how to effectively deliver video streaming contents over cloud computing infrastructures is tackled in this study. Basically, quality of service of video streaming is strongly influenced by bandwidth, jitter and data loss problems. A number of intelligent video streaming algorithms are proposed by using different techniques to deal with such issues. This study aims to propose and demonstrate a novel decision making analysis which combines ISO 9126 (international standard for software engineering) and Analytic Hierarchy Process to help experts selecting the best video streaming algorithm for the case of private cloud computing infrastructure. The given case study concluded that Scalable Streaming algorithm is the best algorithm to be implemented for delivering high quality of service of video streaming over the private cloud computing infrastructure.
An efficient modified video compression HEVC technique based on high quality assessment saliency features presented for the assessment of high quality videos. To create an efficient saliency map we extract global temporal alignment component and robust spatial components. To obtain high quality saliency here, we combine spatial saliency features and temporal saliency features together for different macroblocks in association with transformed residuals. In this way, our saliency model outperforms all the existing techniques. In this paper, we have generated high reconstruction quality video after compression considering SFU dataset. Our experimental result outperforms all the existing techniques in terms of saliency map detection, PSNR and high-resolution quality.
An efficient modified video compression HEVC technique based on high quality assessment saliency features presented for the assessment of high quality videos. To create an efficient saliency map we extract global temporal alignment component and robust spatial components. To obtain high quality saliency here, we combine spatial saliency features and temporal saliency features together for different macroblocks in association with transformed residuals. In this way, our saliency model outperforms all the existing techniques. In this paper, we have generated high reconstruction quality video after compression considering SFU dataset. Our experimental result outperforms all the existing techniques in terms of saliency map detection, PSNR and high-resolution quality.
A REAL-TIME H.264/AVC ENCODER&DECODER WITH VERTICAL MODE FOR INTRA FRAME AND ...csandit
The video coding standards are being developed to satisfy the requirements of applications for
various purposes, better picture quality, higher coding efficiency, and more error robustness.
The new international video coding standard H.264 /AVC aims at having significant
improvements in coding efficiency, and error robustness in comparison with the previous
standards such as MPEG-2, H261, H263,and H264. Video stream needs to be processed from
several steps in order to encode and decode the video such that it is compressed efficiently with
available limited resources of hardware and software. All advantages and disadvantages of
available algorithms should be known to implement a codec to accomplish final requirement.
The purpose of this project is to implement all basic building blocks of H.264 video encoder and
decoder. The significance of the project is the inclusion of all components required to encode
and decode a video in MatLab .
This document discusses video quality analysis for H.264 based on the human visual system. It proposes an improved video quality assessment method that adds color comparison to structural similarity measurement. The method separates similarity measurement into four comparisons: luminance, contrast, structure, and color. Experimental results on video sets with two distortion types show the proposed method's quality scores are more consistent with visual quality than classical methods. It also discusses the H.264 video coding standard and provides examples of encoding and decoding experimental results.
This document discusses post-processing and rate distortion algorithms for the VP8 video codec. It first provides background on the need for post-processing algorithms to reduce blocking artifacts in compressed video, and for rate control algorithms to regulate bitrates and achieve high video quality within bandwidth constraints. It then summarizes existing in-loop deblocking filters and post-processing algorithms. A novel optimal post-processing/in-loop filtering algorithm is described that can achieve better performance than H.264/AVC or VP8 by computing optimal filter coefficients. Finally, a proposed rate distortion optimization algorithm for VP8 is discussed to improve its rate control and coding efficiency.
The proposed scheme embedded the watermark during the differential pulse code modulation process and extracted through decoding the entropy details. This technique utilize the moving picture expert groups standard (MPEG-2) in which discrete cosine transform coefficients are adjusted from selected instantaneous decoder refresh frames for watermarking purpose. The subsets of frames as candidate I-frames are chosen to achieve better perceptibility and robustness. A secret key based cryptographic technique is used to select the candidate frames. Three more keys are required to extract the watermark whereas one of the key is used to stop the extraction process and the remaining two are used to display the scrambled watermark. The toughness is evaluated by testing spatial and temporal synchronization attacks. High sturdiness is achieved against video specific attacks frequently occurs in the real world. Even a single frame can accommodate thousand of watermark bits which reflect that high watermark capacity can be obtained.
This document proposes a method for video copy detection using segmentation, MPEG-7 descriptors, and graph-based sequence matching. It extracts key frames from videos, extracts features from the frames using descriptors like CEDD, FCTH, SCD, EHD and CLD, and stores them in a database. When a query video is input, its features are extracted and compared to the database to detect if it matches any videos already in the database. Graph-based sequence matching is also used to find the optimal matching between video sequences despite transformations like changed frame rates or ordering. The method is shown to perform better than previous techniques at detecting copied videos through transformations.
VIDEO QUALITY ASSESSMENT USING LAPLACIAN MODELING OF MOTION VECTOR DISTRIBUTI...sipij
Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processing.
VQA reflects the quality of a video as most people commonly perceive. This paper proposes a reducedreference
mobile VQA, in which one-dimensional (1-D) motion vector (MV) distributions are used as
features of videos. This paper focuses on reduction of data size using Laplacian modeling of MV
distributions because network resource is restricted in the case of mobile video. The proposed method is
more efficient than the conventional methods in view of the computation time, because the proposed quality
metric decodes MVs directly from video stream in the parsing process rather than reconstructing the
distorted video at a receiver. Moreover, in view of data size, the proposed method is efficient because a
sender transmits only 28 parameters. We adopt the Laplacian distribution for modeling 1-D MV
histograms. 1-D MV histograms accumulated over the whole video sequences are used, which is different
from the conventional methods that assess each image frame independently. For testing the similarity
between MV histogram of reference and distorted videos and for minimizing the fitting error in Laplacian
modeling process, we use the chi-square method. To show the effectiveness of our proposed method, we
compare the proposed method with the conventional methods with coded video clips, which are coded
under varying bit rate, image size, and frame rate by H.263 and H.264/AVC. Experimental results show
that the proposed method gives the performance comparable with the conventional methods, especially, the
proposed method requires much lower transmission data.
A Distortion-Resistant Routing Framework for Video Traffic in Wireless Multih...1crore projects
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Subjective Quality Evaluation of H.264 and H.265 Encoded Video Sequences Streamed Over the Network
1. The International Journal of Multimedia & Its Applications (IJMA) Vol.9, No.3, June 2017
DOI : 10.5121/ijma.2017.9302 15
SUBJECTIVE QUALITY EVALUATION OF H.264 AND
H.265 ENCODED VIDEO SEQUENCES STREAMED
OVER THE NETWORK
Dipendra J. Mandal and Subodh Ghimire
Department of Electrical & Electronics Engineering, Kathmandu University, Dhulikhel,
Nepal
ABSTRACT
Streaming of high-quality video contents as part of multimedia communication, has become essential
nowadays. Video delivered over the network suffers from different kind of impairments, which degrades its
quality. Such network impairments effect differs among different types of codec used. This paper present the
effects of network degradation factors such as packet loss and jitter over H.264 and H.265 encoded video
sequences. In addition to the codec used, we also focused on the different level of temporal and spatial
aspect within the videos. Among different basic test methods, double stimulus impairments scale was used
to complete the experiment as subjective measures of assessment metric from user’s perspective.The result
illustrates that differently encoded video sequences react differently to the network impairments and are
very sensitive to a transmission error. Similarly, it also shows that user’s experience is affected according
to the motion level of video.
KEYWORDS
Video Quality, H.264, H.265, Subjective Evaluation, Packet loss, and Jitter.
1. INTRODUCTION
Due to rapid growth in Internet use and easy access to more users, the deployment of the digitally
compressed video is also rapidly increasing. Distribution of Video content is becoming one of the
most important applications for the real-time system. Visual quality assessment has been so far
one of the most intriguing challenges in the media environment. Quality can be defined in a
number of ways, depending on the application of the multimedia service and the end-user of the
audio-visual content. Network impairments such as packet loss, packet delay, and jitter play a
dynamic role to degrade the quality of received video when transmitted over the network.
Because of variation in coding techniques (such as predictive coding, variable length coding)
used by encoders, different compressed video streams can have different sensitive nature to the
transmission error. In few cases single corrupted bit within the video stream can lose the
synchronization so that even successive correctly received bit becomes useless and result in a
drastic reduction in the quality of the video. Due to which, quality of the video is the major issue.
Digital video quality measurements must be based on the perceived quality of the actual video
being received by the user of the digital video system because the impression of the end user is
nevertheless the important factor [1].
A major challenge for digital video is that raw or uncompressed video requires lots of data to be
stored or transmitted due to which, compression is needed. Compression aims at lowering the
total number of parameters required to represent the signal while maintaining good quality.
Currently, several video compression standards exist for different multimedia applications. Each
standard may be used in a range of applications but is optimized for a limited range. H.261,
2. The International Journal of Multimedia & Its Applications (IJMA) Vol.9, No.3, June 2017
16
H.263, H.263 designed by ITU (International telecommunication Union) is aimed at low-bit-rate
video applications. MPEG standards are defined by ISO (International Organization for
Standardization). MPEG-2 is aimed at high bit rate high-quality applications, and MPEG-4 is
aimed at multimedia applications including streaming video applications on mobile devices [2].
H.264 also referred to as MPEG-4 Part 10 Advanced Video Coding appeared in the mid-2000s. It
has greatly improved the coding performance over MPEG-2 and MPEG-4 Part 2 [2][3], H.264
offers the same compression capability at approximately half the bitrate. Video can be encoded
using the different profiles of H.264 with the baseline profile, the file can be easily played even
on devices with low computational power whereas High and Main profiles target the high
computational devices because of its high compression capabilities. Now, a new standard H.265
also called High-Efficiency Video Coding (HEVC) has been developed that promises a further
factor of two improvements in compression efficiency compared with the previous
standards[5][6].
When network congestion occurs it results to packet drops. The effects of packet loss on real-time
multimedia application are critical and the effect of extensive packet loss on video is acute. If
packet loss happens, some part of the video cannot be decoded and displayed. Similarly, packet
delay variation also known as jitter is variation between packet arrival times at receiving end, this
occurs due to the variability in queuing and propagation delays. A small value of jitter can be
tolerable but increased value causes decoding error and causes quality degradation [7]. Therefore,
it is important to understand the effect of packet loss and jitter on the perceived quality of video
applications.
In this paper, we have presented the details and results of the subjective quality evaluation
performed at the laboratory of Kathmandu University. The aim of this paper is to evaluate the
quality of various encoded video sequence such as H.264 with two different profiles called
Baseline and High and H.265 when streamed over the network. Network effects such as various
levels of packet drop and jitter over the encoded video were studied and compared with the
objective analysis previously performed in our previous work [8].
The rest of the paper is organized as follows: Section II presents the description of various types
of video quality assessments. In section III, we have described the detail of video specification
along with the subjective methods used, laboratory setup and statistical data processing approach.
The result from the experiment with its explanation is presented in section IV. At the end of this
paper, concluding remark is given.
2. VIDEO QUALITY ASSESSMENT
In this section, we have described the theory of different video quality evaluation methodology.
There are two standardized test methods to measure the quality of multimedia experience.
1. Objective Quality Matrix
2. Subjective Evaluation
Objective video quality evaluation techniques are mathematical models that approximate the
results of the subjective quality assessment. A quality matrix such as peak signal to noise ratio
(PSNR), Mean square Error (MSE) and structural similarity (SSIM) are used. These matrices are
evaluated using the computer program and hence is fast and easy to conduct the experiment. In
contrast, subjective video quality experiments involve the human subjects to watch the video
sequence and to rate their quality in numerical values instead of computers to measure the quality
level and results are expressed by the means of mean opinion score (MOS). MOS is an average of
3. The International Journal of Multimedia & Its Applications (IJMA) Vol.9, No.3, June 2017
17
scores provided by the number of users for the same set of experiment. Different types of
subjective methods are explained in the ITU-R. BT. 500 [9] and ITU-T Rec. P.910 [10]. There are
numerous studies performed in a controlled research laboratory setting to evaluate how quality
affects the user experience for different types of media contents. Few of them are described in
[11] [12]
.
There are wide varieties of basic test methods that have been used for the subjective quality
analysis. Some examples are:
Double Stimulus Continuous Quality Scale (DSCQS): Reference and test video
sequences are shown twice to the observer and are asked to rate the quality.
Double Stimulus Impairment Scale (DSIS): Reference and test video sequence are
shown only once and are asked to grade the video. This type of methods is well
suited for clearly visible impairments such as artifacts caused by transmission errors.
Single Stimulus Continuous Quality Evaluation: In this method instead of showing
the separate short video sequence pair, observers are shown a video session of
typically 20-30 minutes also reference video is not shown.
Absolute Category Rating: This is the single- stimulus method. Observers rate each
test video sequences individually without comparison to an explicit reference. Since
no reference is shown, from a time perspective it is considered as efficient method
over DSIS or DSCQS.
Pair Comparison: In this, test videos taken from the same source but under different
conditions are paired in many possible combinations and observers are asked to make
a preference judgment for each pair.
All above test methods have different rating scale which includes both continuous and discrete
level and are used for different application [13] and a minimum of 15 observers are recommended
in order to conduct the experiment. Details about rating scale and analysis of above methods are
discussed in [14].
3. EXPERIMENT DESIGN AND EVALUATION
In this section details of video sequences used for the test is explained. We have further
introduced the design and facts of subjective methods and statistically data processing methods
used and also all the specifics related to the laboratory setup has been discussed.
3.1. VIDEO SEQUENCES
We have used the two videos for this experiment. Both the video sequence has encoded using the
H.264 baseline, H.264 High profile and H.265 and has a different level of motion. These video
clips are reconstructed and captured after being streamed over the network having a different
level of packet drops and packet variation. Details of the video network emulation setup and
procedure used to obtain video sequences can be seen in [8].Table 1 shows the details of encoded
parameters for video samples.
4. The International Journal of Multimedia & Its Applications (IJMA) Vol.9, No.3, June 2017
18
Table 1. Video encoding parameters
Video Files /
Parameters
Video_1 Video_2 Video_3
Codec H.264 H.264 H.265
Profile Baseline High -
No. of frame 270 270 270
Frame Resolution 1280*720 1280*720 1280*720
Frame Rate 29.97 29.97 29.97
Chroma Format 4:2:0 4:2:0 4:2:0
No. of I, P & B
frames
I=18,
P=252
I=2,
P=268
I=2, P=40,
B=228
Duration 9.74 sec 9.74 sec 9.74 sec
File Size 25.0 MB 1.26 MB 196KB
3.2. SUBJECTIVE QUALITY EVALUATION
The method we selected for our experiment is DSIS. In this method, two videos were shown to
the observer, one the unimpaired (reference video) and the impaired (test) video. The reference
video was made known to the observer and shown before the test video. The general arrangement
of a test system for this method is shown in figure 1. After viewing the both video sequence, the
observer was asked to grade the test video compared to the reference. The grade scale used is
given in table 2 and presentation of video sequence used in this experiment is shown in figure 2.
Table 2. Grade scale with description for DSIS methods
Scale Description
5 Imperceptible
4 Perceptible but not annoying to observer
3 Slightly annoying to observer
2 Annoying to observer
1 Very annoying
Figure 1. Arrangement for test system for DSIS method
5. The International Journal of Multimedia & Its Applications (IJMA) Vol.9, No.3, June 2017
19
Figure 2. Presentation sequence with duration used in experiment
3.3. LABORATORY EXPERIMENTAL SETUP
The test has been carried out according to the recommended guidelines of BT.500-11 [9], which
suggest standard viewing conditions, criteria for the observer and test material selection including
experimental procedure and data analysis methods. Observer was seated directly in line with the
center of the video display monitor at a specified distance, which was 7-8H where H is the height
of the LCD screen. Details of LCD display device used during the experiment is provided in table
3. The sets of the video were evaluated in an experiment performed at the laboratory of
Kathmandu University, Nepal. Observer taking part in the experiment were from the Department
of Electrical and Electronics mostly students of undergraduates and faculty. The subject pool
consisted both male and female and each was tested for color blindness, this was performed using
a Snellen chart [15], color blindness using Ishihara plates [16]. Each observer was briefed about
the test prior to the experiment with short training session of 6-10 minutes. This was done in
order to stabilize the observers and data issued from this session was not included. Training
session includes an explanation about what the observer are going to see, what they have to
evaluate and how to evaluate the video with a different level of distortion. Distorted video
sequences were shown in random order. The experiment was running over 5 days with 6-8
observers per day this was done to avoid the viewer’s exhausting which may affect their
evaluation ability. Figure 3 shows the picture of laboratory setup for performing the subjective
test experiment.
Table 3. Testbeds screen details
Size 18.5 Inches
Resolution 1366*768
Contrast Ratio 5000:1
Screen Mode WXGA
Aspect Ratio 16:9
Brightness 250/m2
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Figure 3. Subjective test laboratory setup picture
3.4. STATISTICAL DATA PROCESSING
In our subjective lab experiment, altogether 34 observers participated, they rated 62 video
sequences corresponding to the two different video contents reconstructed after a different level
of network impairments. Out of 34 observers one was found to be color blind and reading from
him was not taken for the calculation. Data obtained from the observer was obtained was an
integer value between 1 to 5 and need to be summarized using statistical techniques. For this first
normality test was performed to verify the obtained data and this was done using the β2 test
(Kurtosis coefficient of the function) described in ITU-R BT.500-11. If the value falls between 2
and 4 then the distribution is normal. Scores provided by the observer was found to be close to the
normal distribution. Secondly, the outlier detection was performed for each observer. This is
described in Annex 2 of ITU-R BT.500-11. If the score of observer deviates strongly from the
average score for given presentation, then the observer was discarded. A maximum of 3 observers
was discarded after performing this analysis. Finally, after the filtration of raw data, mean opinion
score (MOS) for each video sequence was evaluated, which is mathematically expressed in
equation 1.
(1)
Where,
N: is the total no. of the valid observer after discarding the outlier.
Observer = i= 1, 2…... N
Test Sequence = j= 1, 2…... N
Oij: is the score provided by observer i for test sequence j
Finally, MOS for each video sequences were plotted using the confidence interval (CI) which is
derived from the standard deviation and size of the sample. For the normally distributed data with
sample size N, MOSj and standard deviation Sj, CI is defined as , where is
expressed in equation 2.
(2)
For the subjective quality data analysis, it is recommended to calculate the 95 % CI for each point
of the test. For 95 % CI with a normal distribution, the value of z in equation 2 is 1.96.
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4. RESULTS AND DISCUSSION
In the initial phase of our experiment, we studied the effect of packet drop over MOS for video
with a different level of motion streamed over the network. Figure 4 shows the effect of packet
drop over MOS for both high and low level of temporal variation in the video. The outcome
shows that video with low temporal variation has better MOS over high temporal variation
video.The plot also shows the CI result, as it can be seen from small confidence interval, the
result obtained from different subjects are reliable and also variation between the score obtained
from various subjects are very small.
Figure 4. Packet drop over MOS for video with low and high level of motion (95 % CI)
We also studied the effect of packet drop over user opinion for H.264 with Baseline profile,
H.264 with High profile and H.265 encoded video sequence. Figure 5 and figure 6 demonstrate
the result for both low and high motion video respectively. It shows that codec with high
compression efficiency i.e. H.265 is affected more severely for given packet drop as compared to
H.264 with a different profile. The main reason behind this is due to the difference in a group of
picture structure for a different encoded video sequence.
Figure 5. MOS Vs. Packet drop for three different encoded video sequences (Low motion)
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Figure 6. MOS Vs. Packet drop for three different encoded video sequences (High motion)
To analyze the effect of jitter on the video quality from a user perspective, few tests were made in
which observer rated the video sequence with different level of jitter variation i.e. we repeated the
measurement for 1, 5, 10, 20, 50, 100, 200, 500 and 1000 millisecond delay variation. The result
shows that video with the high temporal aspect is more sensitive to increased jitter level
compared to low temporal aspect video sequences. This is lustrated in figure 7 with 95% CI,
which shows that the result obtained from different subjects are reliable.
The experiment also shows that effect of jitter for video sequence encoded with different video
codec when transmitted over the network. Graphical representation plotted from the data obtained
is shown in figure 8. The result shows that the video encoded with baseline profile of H.264 is
more prone to the added jitter compared to others. Result also shows that jitter has an almost
negligible effect on all three encoded video sequences up to 10 ms of added jitter to the network
after that H.264 encoded video with profile baseline is affected drastically in compare to other
two. Also in presence of high jitter even other two encoded video sequences are affected equally.
Figure 7. Result of Jitter over MOS for video with two different level of motion
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Figure 8. Effect of jitter over MOS for three different video codec
5. CONCLUSION
In this paper, we presented a detailed description of subjective video quality assessment
performed for three different encoded video streams when streamed over the error-prone network.
The results obtained is close to testing output obtained in our previous work using objective
evaluation. The obtained result shows that video encoded with H.265 is affected more with packet
drop compared with its predecessors. However, in presence of network jitter, H.264 encoded
videos suffers more. This clearly indicates that although newly developed codec H.265 is twice
bandwidth efficient than H.264 but is more sensitive to the network error and suffers more
severely when a small amount of error is introduced into the network.
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AUTHORS
Dipendra Jee Mandal received his bachelor in Electrical and Electronics Engineering with
specializing in Communication from Kathmandu University, Dhulikhel, Nepal in 2010. He
is currently enrolled as MS by Research student at same university. He worked as
researcher at Department of Electrical and Electronics Engineering, Kathmandu University
from 2010 to 2013. His current research interest is video coding, quality assessment and
digital image processing.
Subodh Ghimire received his bachelor in Electrical and Electronics Engineering with
specialize in Communication from Kathmandu University(KU), Dhulikhel, Nepal in 2001
and MS in Electrical in 2003 from San Jose State University, CA, USA. He is currently
working as Assistant Professor at Department of Electrical and Electronics Engineering,
KU.
The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.