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Efficient storing and retrieval of medical images has direct impact on reducing costs and improving access in cloud-based health care services. JPEG 2000 is currently the commonly used compression format for medical images shared using the DICOM standard. However, new formats such as high efficiency video coding (HEVC) can provide better compression efficiency compared to JPEG 2000. Furthermore, JPEG 2000 is not suitable for efficiently storing image series and 3-D imagery. Using HEVC, a single format can support all forms of medical images. This paper presents the use of HEVC for diagnostically acceptable medical image compression, focusing on compression efficiency compared to JPEG 2000. Diagnostically acceptable lossy compression and complexity of high bit-depth medical image compression are studied. Based on an established medically acceptable compression range for JPEG 2000, this paper establishes acceptable HEVC compression range for medical imaging applications. Experimental results show that using HEVC can increase the compression performance, compared to JPEG 2000, by over 54%. Along with this, a new method for reducing computational complexity of HEVC encoding for medical images is proposed. Results show that HEVC intra encoding complexity can be reduced by over 55% with negligible increase in file size.

Safety concerns within the substation, such as fractures, burns and wasp stings caused in the field, are addressed with the proposed solution. Risks can be avoided through tracking and communication. This paper focuses on safety improvement and increasing work efficiency in the electric substations and an overview of the entire system and its functions are presented. The extension of the hard hat was designed on Tinkercad, the 3Dprinted ring can be placed on a typical hardhat. In sequel, the corresponding mobile application is created using the Xcode 8 program and Swift 3 programming language. Feedback from utilities workers is incorporated to modify the wireless technology. Overall, the S&S Tractus incorporates six features which allow workers to complete their tasks safer and more efficiently than ever before. It provides early warnings of potential safety issues, making interdepartmental phone calls, speech recognition for note taking, a global positioning system (GPS), photo taking and headlamp adjustments.

VC-1 is one of the three video coding standards for Blu-ray DVD, which also includes MPEG-2 and H.264. In this paper, an efficient transcoding algorithm from VC-1 video to H.264 video is discussed, we present a transcoder that addresses I, P, B, and interlaced pictures. The main differences between the two standards are analyzed and a solution for transforming from one to the other is presented. The paper proposes a pixel domain transcoder which exploits the variable size transform used in VC-1 to select the variable block size for motion compensation in H.264. We also discuss transcoding of high profile video features; in particular, adaptive size transform and interlacing. Experimental results show that the pixel domain approach reduces computational complexity by more than 50 % as compared to a cascaded one with negligible drop in peak-signal-to-noise-ratio (PSNR).

Managing Information Technology (IT) assets in data centers is a time consuming and error prone process. IT personnel typically identify misplaced assets manually by cross checking and visually inspecting assets. An automated way of keeping track of assets using portable devices reduces human error and improves productivity. The proposed asset management application on the tablet captures images of assets and searches an annotated database to identify the asset. Matching performance and response time of asset matching is evaluated using three different image feature descriptors. Methods to reduce feature extraction and matching complexity were developed. Performance and accuracy tradeoffs were studied, domain specific problems were identified, and optimizations for portable platforms were made. The results show that the proposed methods reduce complexity of asset matching by 67 % when compared to the matching process using standard image feature descriptors.

The High Efficiency Video Coding (HEVC) standard roughly doubles the rate-distortion compression performance of its predecessor, H.264/AVC, at a cost of a high computational complexity. Moreover, intra sequences are commonly used at video editing or post-production, making it necessary a migration from H.264/AVC to HEVC. This paper aims to propose a fast intra transcoding algorithm from H.264/AVC to HEVC.

We evaluate the performance of HEVC Intra and Inter coding of Digital Imaging and Communications in Medicine (DICOM) standard based medical images in both lossless and lossy mode compared to JPEG 2000 (JP2). Prior studies have used Main Still Profile to evaluate lossless HEVC Intra coding whereas we have used HEVC Range extension Profiles. Besides lossless Intra coding, our study evaluates lossless Inter prediction of DICOM based medical image series. The results show that HEVC Inter Prediction achieves reduction in file size up to 39% for the lossless inter mode and up to 94% for lossy mode in case of images with low loss accepted in some medical imaging (PSNR greater than 50 dB). This reduction of file size is significant and can be used to reduce transmission and storage cost especially in a cloud based e-healthcare scenario.

The articles in this special issue focus on technologies and applications that support perception inspired video processing technologies.

With continued increase in the use of smartphones, user expectations of content access have also increased. Most of the content that exists today is not designed for mobile devices. Mobile devices cannot directly access most of the content due to the mismatch in device capability and content playback requirements. Content adaptation is an essential tool that bridges the gap between device capabilities and content formats. In this paper we present an overview of content adaptation and survey recent papers on content adaptation for mobile devices. We introduce the when, where, and what of content adaptation to help classify the content adaptation techniques and to select the appropriate techniques for a given content delivery environment.

Computing capabilities are continuing to increase with the availability of multi core and many core processors. The wide availability of multi core processors has made parallel programming possible for end user applications running on desktops, workstations, and mobile devices. While parallel hardware has become common, software that exploits parallel capabilities is just beginning to take hold. Multimedia applications, with their data parallel nature and large computing requirements will benefit significantly from parallel programming. In this paper an overview of parallel programming is presented and languages and tools for parallel programming such as OpenMP and CUDA are introduced within the scope of multimedia applications.

Based on our previous efforts, we introduce in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain. Machine learning tools are used to exploit the correlation between the macroblock (MB) decision of the H.264 video format and the distribution of the motion compensated residual in MPEG-2. Moreover, a dynamic motion estimation technique is also proposed to further speed-up the decision process. Finally, we go a step further on our previous research efforts by combining the two aforementioned speed-up approaches. Our simulation results over more than 40 sequences at common intermediate format and quarter common intermediate format resolutions show that our proposal outperforms the MB mode selection of the rate-distortion optimization option of the H.264 encoder process by reducing the computational requirements by up to 90%, while maintaining the same coding efficiency. Finally, we conduct a comparative study of our approach with the most relevant fast inter-prediction methods for MPEG-2 to H.264 transcoder recently reported in the literature.

The authors develop a decoder/encoder system (transcoder) to solve the consumption constraint in the communications between end-user devices, when a new Wyner-Ziv (WZ)/H.264 framework is defined for being used in mobile-to-mobile environments. This approach is based on leaving to the devices only WZ video encoding and traditional video decoding; the lowest complexity algorithms in both paradigms. The system shifts the burden of complexity to the network, where an improved transcoder that reuses information between both paradigms is allocated. The WZ decoding motion vectors are used to reduce the H.264 motion estimation process. The proposed transcoder offers a complexity reduction up to 60- on average, without any rate distortion drop.

This paper proposes a Wyner-Ziv/H.264 transcoder that enables low cost video applications. The proposed solution supports video encoding on resource constrained devices such as disposable video cameras, network camcorders and low cost video encoders. This approach is based on reducing encoding resource requirements on a device by using Wyner-Ziv video encoding. The system shifts the burden of complexity away from the encoder, for example to a network node, where a transcoder efficiently converts WZ encoded video to H.264 by reusing the information from the WZ decoding stage. The transcoded H.264 video is requires fewer resources than WZ decoding and therefore reduces the complexity of decoding. The complexity of encoding and playback ends of video applications is thus reduced enabling new class of consumer application. The paper is focused on reducing the complexity of the macro-block mode coding decision process carried out in H.264 encoding stage of the transcoder. Based on a data mining process, the approach replaces the high complexity H.264 mode decision algorithm by a faster decision tree. The proposed architecture reduces the battery consumption of the end-user devices and the transcoding time is reduced by 86% with negligible rate-distortion loss.

In this paper we introduce and evaluate a novel machine learning based approach to reduce the complexity of Intra macroblock (MB) coding. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264/AVC video have a correlation with the intensities of adjacent MBs and sub-MBs. This paper also discusses and analyzes different approaches of using machine learning in Intra prediction. We discuss, amongst other features, slices, Intra prediction scheme for H.264 and data mining. We use data mining algorithms to develop decision trees for H.264 coding mode decisions. The proposed approach reduces the H.264/AVC MB mode computation process into a decision tree lookup with very low complexity. The proposed algorithm is implemented in reference software by modifying the source code and is compared with the JM reference software for H.264/AVC.

The efficiency of H.264 video compression together with the wide use of MPEG-2 today necessitates transcoding between these two technologies. The combination of bandwidth efficient technology such as H.264 and the widely deployed MP EG-2 allows for innovative video delivery services that can exploit both these technologies. Using H.264 for video delivery and MPEG-2 for playback on the end user equipment fully leverages the benefits of these technologies. This architecture requires transcoding H.264 video delivered over networks to MPEG-2 format for playback on the end- user device. We present algorithms for transcoding H.264 to MPEG-2 and evaluate the performance of these algorithms. The performance is compared with a baseline transcoder with a full decoding stage and a full encoding stage. The proposed algorithms reduce the complexity by reducing the search space for motion estimation. The results show that the proposed algorithms reduce the motion estimation complexity by over 80% with a negligible loss in PSNR.

The H.264 standard achieves much higher coding efficiency than the MPEG-2 standard, due to its improved inter-and intra-prediction modes at the expense of higher computational complexity. Transcoding MPEG-2 video to H.264 is important to enable gradual migration to H.264. However, given the significant differences between the MPEG-2 and the H.264 coding algorithms, transcoding is a much more complex task and new approaches to transcoding are necessary. The main problems that need to be addressed in the design of an efficient heterogeneous MPEG-2/H.264 transcoder are: the inter-frame prediction, the transform coding and the intra-frame prediction. In this paper, we focus our attention on the inter-frame prediction, the most computationally intensive task involved in the transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for P-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast MB mode estimation would lead to reduced complexity. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in MPEG-2 video. We use machine learning tools to exploit the correlation and construct decision trees to classify the incoming MPEG-2 MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the MB mode selection complexity by as much as 95% while maintaining the coding efficiency. Finally, we conduct a comparative study with some of the most prominent fast inter-prediction methods for H.264 presented in the literature. Our results show that the proposed approach achieves the best results for video transcoding applications.

Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than previous standards (such as H.263, MPEG-2, MPEG-4, etc.), due to its improved inter- and intraprediction modes at the expense of higher computation complexity. It is expected that H.264/AVC will take over the digital video market, replacing the use of previous standards in most digital video applications. This creates an important need for heterogeneous video transcoding technologies from older standards to H.264. In this paper, we focus our attention on the interframe prediction, the most computationally intensive task involved in the heterogeneous video transcoding process. This paper presents a novel macroblock (MB) mode decision algorithm for interframe prediction based on data mining techniques to be used as part of a very low complexity heterogeneous video transcoder. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264 video have a correlation with the distribution of the motion compensated residual in the decoded video. We use data mining tools to exploit the correlation and derive decision trees to classify the incoming decoded MBs into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. For general validation purposes, we apply our algorithm to two of the most important heterogeneous video transcoders: MPEG-2 to H.264 and H.263 to H.264. Our results show that the our data-mining based transcoding algorithm is able to maintain a good video quality while considerably reducing the computational complexity by 72% on average when applied in MPEG-2 to H.264 transcoders, and by 62% on average when applied in H.263 to H.264 transcoders. Finally, we conduct a comparative study with some of the most prominent fast interprediction methods for H.264 presented in the literature. Our results show that the proposed data mining-based approach achieves the best results for video transcoding applications.

Recent developments have given birth to H.264/AVC: a video coding standard offering better bandwidth to video quality ratios than MPEG-2. It is expected that the H.264/AVC will take over the digital video market, replacing the use of MPEG-2 in most digital video applications. The complete migration to the new video-coding algorithm will take several years given the wide scale use of MPEG-2 in the market place today. This creates an important need for MPEG-2/H264 transcoding technologies. However, given the significant differences between both encoding algorithms, the transcoding process of such systems is much more complex to other heterogeneous video transcoding processes. In this work, we start by analyzing the methods defined in the H.264 video coding standard for the intra prediction: a central element of every H.264 encoder. We then introduce and evaluate six fast intra mode decision algorithms which should enable the development of MPEG-2 to H.264 transcoders. Having evaluated all the proposed methods, we have come out with a high-efficient method, namely DC-ABS pixel. Our results show that our algorithm considerable reduces the complexity involved in the intra prediction with respect the mode decision algorithms used in H.264 JM reference software, while exhibiting a slight degradation on the RD function.. Finally, we analyze a comparative study with two of the most prominent fast intra prediction methods presented in the literature. The results show that the proposed DC-ABS pixel method achieves the best results for video transcoding applications.

In this work we propose a novel type of digital video encryption that has several advantages over other currently available digital video encryption schemes. We also present an extended classification of digital video encryption algorithms in order to clarify these advantages. We analyze both security and performance aspects of the proposed method, and show that the method is efficient and secure from a cryptographic point of view. Even though the method is currently feasible only for a certain class of video sequences and video codecs, the method is promising and future investigations might reveal its broader applicability. Finally, we extend our approach into a novel type of digital video steganography where it is possible to disguise a given video with another video.

The H.264 standard achieves much higher coding efficiency than the MPEG-2 standard, due to its improved inter and intra prediction modes which come with a cost of higher computation complexity. Transcoding MPEG-2 video to H.264 is important to enable gradual migration to H.264. However, given the significant differences between the MPEG-2 and the H.264 coding algorithms, transcoding is much more complex and new approaches to transcoding are necessary. In this paper, we introduce and evaluate a low complexity macroblock partition mode decision algorithm, to be used as part of a high-efficient inter-frame prediction in MPEG-2 to H.264 transcoder. The proposed tools are used to compute an optimal MB coding mode decision with significantly reduced computational complexity. Specifically, we achieve the computational savings by using the following MB information coming from MPEG-2: the MB coding modes, the coded block pattern (CBPC) in MPEG-2, and the mean and variance of the 16 4?×?4 sub blocks of the MPEG-2 residual MBs. We use data mining algorithms to develop a decision tree for H.264 coding mode decisions. The decision trees are built using RD optimized mode decisions and result in highly efficient mode decisions, with significantly reduced computational complexity. The proposed transcoder is 35% faster than the RD optimized H.264 reference transcoder without a significant PSNR degradation (0.05 dB on average). The proposed transcoder performs over 0.4 dB better on average than the SAE cost based H.264 transcoding.

The ability to assess the quality of new multimedia tools and applications relies heavily on the perception of the end user. In order to quantify the perception, subjective tests are required to evaluate the effectiveness of new technologies. However, the standard for subjective user studies requires a highly controlled test environment and is costly in terms of both money and time. To circumvent these issues we are utilizing crowdsourcing platforms such as CrowdFlower and Amazon's Mechanical Turk. The reliability of the results relies on factors that are not controlled and can be considered “hidden”. We are using pre-test survey to collect responses from subjects that reveal some of the hidden factors. Using statistical analysis we build parameterized model allowing for proper adjustments to collected test scores.

The H.264 video coding standard, also known as MPEG AVC, represents the state of the art in video compression. This article provides an overview of the H.264 standard. To help the reader understand the similarities and differences between it and MPEG-2 (and other hybrid video coding standards), the author describes the H.264 features and compare them to functionally similar MPEG-2 features

This paper presents an MPEG-2 to H.264 intra frame video transcoder. The DCT coefficients gathered from the MPEG-2 process are used to estimate the directional features in a picture. The estimated directional features are then used to compute the intra prediction modes in the H.264 encoding stage. The INTRA 16/spl times/16 vs. INTRA4/spl times/4 MB coding mode decision is made based on the variance of the DC coefficients of the 8/spl times/8 DCT blocks in the input MPEG-2 video. The proposed approach completely avoids the MB mode and prediction mode computations in the H.264 encoding stage, there by reducing the transcoder complexity substantially. The results show that our approach results in over 30% drop in the intra frame encoding time with a negligible drop in PSNR. A comparison with a cascaded reference transcoder is presented.

With available data rates for mobile devices constantly increasing, services such as video broadcast and multicast are becoming feasible. A new standard called Multimedia Broadcast Multicast Services (MBMS) is being developed by 3GPP to enable new class of spectrum-efficient multimedia services. Multicast services are expected to serve a diverse user base with varying connectivity and capabilities. We analyze the problem of video error resilience in MBMS services that is critical to maintain consistent quality for end users. The existing error resilience techniques for IP multicasting are not applicable in the MBMS environment. In this paper, we present error resilience techniques that are applicable within the context of the MBMS standard. We propose an Intra Block Refresh method for MBMS services and the results show improved performance. We develop a methodology that can be applied to adapting traditional error resilience tools for the MBMS environment.

Object-based representation of audio-visual (AV) presentations provides a flexible scheme to create interactive content that lends itself to resource-driven adaptation. The content adaptation needs of mobile devices can be met well with the use of object-based AV presentations. The main distinguishing feature of object-based AV presentations is the scene composition at the user terminal. In this paper, we discuss the problem of scheduling the delivery of object-based AV presentations under resource constraints. We explore the similarities with the problem of job sequencing on a single machine. We present a family of algorithms to determine the schedulability of AV presentations, and for unschedulable presentations, we present algorithms to compute a schedule that minimizes the additionally acquired resources. We present algorithms for computing incremental schedules for applications such as content authoring that require immediate feedback on resource consumption. The algorithms can be used to schedule object-based MPEG-4 presentations. We discuss the algorithms and results by considering a relatively complex MPEG-4 presentation with 16 objects, including audio, video, and images.

This is a MM'14 Workshop Summary Abstract for PIVP'14 - 1st International Workshop on Perception Inspired Video Processing. Workshop provided a venue for researchers involved with perceptual video processing and coding to present their current work and to discuss future directions. The workshop program consisted of a keynote talk, oral presentations of full papers and interactive poster session for the short papers. All participants took part in the discussion panel at the end of workshop, exchanging ideas and suggestions for future work.

High Efficiency Video Coding (HEVC) is the new video coding standard recently approved by ISO and ITU. HEVC allows a bit rate reduction greater than a 50% with respect to its predecessor, the H.264/AVC, offering the same perceptual quality, by means of a set of new tools that have been introduced. Compared with the current state of the art in image coding, such as JPEG, JPEG2000 or the new JPEG XR, the new Intra Frame coding performs a high compression process in the "All-Intra" mode. All these improvements are at expense of a high computational cost, making it considerably difficult to implement in real time. Hence, this paper presents a mechanism that can be used by the RDO algorithm to select the optimal coding block size for Intra-Prediction, by using a data mining classifier, based on a previous training. Experimental results show that the proposed algorithm can achieve a 30% of Time Savings over a wide range of high resolution sequences (Class A, B and F), with a negligible loss of coding efficiency.

A large number of health-related applications are being developed using web infrastructure. Video is increasingly used in healthcare applications to enable communications between patients and care providers. We present a video conferencing system designed for healthcare applications. In face of network congestion, the system uses role-based adaptation to ensure seamless service. A new web technology, WebRTC, is used to enable seamless conferencing applications. We present the video conferencing application and demonstrate the usefulness of role based adaptation.

We describe the implementation of a streaming client-server system for object-based audio-visual presentations in general and MPEG-4 content in particular. The system augments the MPEG-4 demonstration software implementation (IM1) for PCs by adding network-based operation with full support for the Delivery Multimedia Integration Framework (DMIF) specification, a streaming PC-based server with DMIF support (via Xbind Inc.'s XDMIF suite), and multiplexing software. We describe XDMIF, the first reference implementation of the DMIF specification. The MPEG-4 server is designed for delivering object-based audio-visual presentation. We discuss the issues in the design and implementation of MPEG-4 servers. The system also implements a novel architecture for client-server interaction in object-based audio-visual presentations, using the mechanism of command routes and command descriptors. This new concept of command routes and command descriptors is useful in developing sophisticated interactive applications.

Delivery of audio-visual objects differs from the traditional video on demand systems in the characteristics of the presentations delivered. Video on demand has mainly been about delivering MPEG-2 audio and video. In the case of object based presentations such as MPEG-4 presentations, the media data and the media composition data are transmitted to a client as separate streams, typically with different QoS requirements, in the same session. Furthermore, as the number of objects in a presentation can be quite large, the overhead required to manage the session is large. Interactivity makes this problem more complex as the resources required for a session will now depend on the user behavior, especially when user interaction with objects changes the number of objects in the scene either by adding or deleting objects. In this paper, we present our recent work on delivering audio-visual services based on MPEG-4. The issues covered include object-based audio-visual (MPEG-4) servers, object scheduling, object-to-object communications, and content creation.

To promote the development and deployment of broadband audio visual services, DAVIC has been working on specifying the standards and interfaces for end-to-end systems providing services to consumers over broadband networks. DAVIC specifies tools to provide these broadband services. Since the tools specified by DAVIC are standardized/developed by independent organizations, it is important to verify that the complete system works when these tools are used together. An interoperability event was organized by Columbia University for this very purpose. In this paper we first introduce DAVIC and DAVIC systems and then give details of the interoperability event: the motivation, goals, and results.

This paper reports our progress in developing an advanced video-on-demand (VoD) testbed, which will be used to accommodate various multimedia research and applications such as Electronic News on Demand, Columbia‘s Video Course Network, and Digital Libraries. The testbed supports delivery of MPEG-2 audio/video stored as transport streams over various types of networks, e.g., ATM, Ethernet, and wireless. Both software and hardware video encoders/decoders are used in the testbed. A real-time video pump and a distributed application control protocol (MPEG-2‘s DSM-CC) have been incorporated. Hardware decoders and set-tops are being used to test wide-area video interoperability. Our VoD testbed also provides an advanced platform for implementing proof-of-concept prototypes of related research. Our current research focus covers video transmission with heterogeneous quality-of-service (QoS) provision, variable bitrate (VBR) traffic modeling, VBR server scheduling, video over Internet, and video transmission over IP-ATM hybrid networks. An important aim is to enhance interoperability. Accommodation of practical multimedia applications and interoperability testing with external VoD systems has also been undertaken.

In this paper, we present a subjective video quality evaluation system that has been integrated with different crowdsourcing platforms. We try to evaluate the feasibility of replacing the time consuming and expensive traditional tests with a faster and less expensive crowdsourcing alternative. CrowdFlower and Amazon's Mechanical Turk were used as the crowdsourcing platforms to collect data. The data was compared with the formal subjective tests conducted by MPEG as part of the video standardization process, as well as with previous results from a study we ran at the university level. High quality compressed videos with known Mean Opinion Score (MOS) are used as references instead of the original lossless videos in order to overcome intrinsic bandwidth limitations. The bitrates chosen for the experiment were selected targeting Internet use, since this is the environment in which users were going to be evaluating the videos. Evaluations showed that the results are consistent with formal subjective evaluation scores, and can be reproduced across different crowds with low variability, which makes this type of test setting very promising.

Video consumption patterns continue to change with consumers relying more and more on on-demand Internet video and portable devices rather than traditional TV services. This new form of video service delivery and consumption makes possible more interactive and social experiences for video consumers, commonly referred to as Social TV services. This paper presents an overview of technologies and guidelines for the development of Social TV applications. A prototype using three core technologies, WebRTC, DASH, and WebSocket was developed to understand the challenges and demonstrate the feasibility of such applications.

This paper presents a model and approach to select an efficient set of HEVC encoding options for mobile devices. The main goal is to reduce the encoding complexity without significantly affecting the quality of video conferencing applications. Video target bit rates from 100kbps to 600kbps were used within this study. Experimental results show that by carefully selecting the coding unit size, coding unit depth and to a lesser degree transform unit size, the encoder computational complexity can be reduced for the target bit rates while maintaining an allowable additional PSNR loss. Results show a 36.5% of complexity reduction on average with a negligible PSNR loss of less than 0.25 dB.

Mobile compute environments provide a unique set of user needs and expectations that designers must consider. With increased multimedia use in mobile environments, video encoding methods within the smart phone market segment are key factors that contribute to positive user experience. Currently available display resolutions and expected cellular bandwidth are major factors the designer must consider when determining which encoding methods should be supported. The desired goal is to maximize the consumer experience, reduce cost, and reduce time to market. This paper presents a comparative evaluation of the quality of user experience when HEVC and AVC/H.264 video coding standards were used. The goal of the study was to evaluate any improvements in user experience when using HEVC. Subjective comparisons were made between H.264/AVC and HEVC encoding standards in accordance with Doublestimulus impairment scale (DSIS) as defined by ITU-R BT.500-13. Test environments are based on smart phone LCD resolutions and expected cellular bit rates, such as 200kbps and 400kbps. Subjective feedback shows both encoding methods are adequate at 400kbps constant bit rate. However, a noticeable consumer experience gap was observed for 200 kbps. Significantly less H.264 subjective quality is noticed with video sequences that have multiple objects moving and no single point of visual attraction. Video sequences with single points of visual attraction or few moving objects tended to have higher H.264 subjective quality.

In this paper we present work on exploiting visual temporal masking phenomenon applied to video compression. Our results show that it is possible to reduce bitrate of the compressed video sequence without affecting subjective quality and quality of experience (perceptually lossless). The principles we present here are applicable to all modern hybrid coding systems and can be implemented seamlessly with video delivery platforms. Results show up to 6% of additional savings when implemented on top of the state of the art encoder.

This study analyzes a use case where computer clouds are used in the transcoding of media content and points to their advantages in a low cost media distribution solution such as HTTP Live.

Media content distribution poses different challenges when it comes to supplying the variety of existing media formats requested by end-users. The need for an on demand transcoding stage may be justified under specific circumstances but is hampered by the high latency and long processing times. The great power cloud computing offers can be leveraged in a situation like this. This paper analyzes a use case where a Hadoop-based computer cloud is used as an aid in the transcoding of media content. The results point to the suitability of such a resource in a media distribution process requiring low cost streaming, such as the HTTP Live streaming scenario which supports streaming of audio or video to the iPhone, iPad, iPod touch and desktop computers as well as the transfer of video on demand with encryption and authentication.

Games have become important applications on mobile devices. A mobile gaming approach known as remote gaming is being developed to support games on low cost mobile devices. In the remote gaming approach, the responsibility of rendering a game and advancing the game play is put on remote servers instead of the resource constrained mobile devices. The games rendered on the servers are encoded as video and streamed to mobile devices. Mobile devices gather user input and stream the commands back to the servers to advance game play. With this solution, mobile devices with video playback and network connectivity can become game consoles. In this paper we present the design and development of such a system and evaluate the performance and design considerations to maximize the end user gaming experience.

In this paper, we show that it is possible to reduce the complexity of Intra MB coding in H.264/AVC based on a novel chance constrained classifier. Using the pairs of simple mean-variances values, our technique is able to reduce the complexity of Intra MB coding process with a negligible loss in PSNR. We present an alternate approach to address the classification problem which is equivalent to machine learning. Implementation results show that the proposed method reduces encoding time to about 20% of the reference implementation with average loss of 0.05 dB in PSNR. Resource constrained devices typically manage the complexity by using a subset of possible coding modes thereby sacrificing video quality. This quality and complexity relationship is evident in most video codecs used today. Most H.264/AVC encoder implementations on mobile devices currently do not implement the standard profiles fully due to high complexity. For example Intra 4x4 prediction is usually not implemented due to complexity. Most of the traditional complexity reduction approaches in video coding are based on eliminating a subset of allowed coding modes and sacrifice quality for reduced complexity. The traditional approaches have not been able to reduce the encoding complexity enough to enable the use of advanced video coding features on resource constrained devices. We developed a machine learning based approach to reduce the computationally expensive elements of encoding such as coding-mode evaluation to a classification problem with much less complexity. 1 We successfully adopted this technique for transcoding and produced much better results. 2 We continued to improve our methodology by employing various techniques to improve our results in the classification domain. The key contribution of this work is the exploration of machine learning and mathematical formulation of video coding mode decision as a classification problem.

This chapter discusses an emerging concept of a cloud computing based Patient Centric Medical Information System framework that will allow various authorized users to securely access patient records from various Care Delivery Organizations (CDOs) such as hospitals, urgent care centers, doctors, laboratories, imaging centers among others, from any location. Such a system must seamlessly integrate all patient records including images such as CT-SCANS and MRI’S which can easily be accessed from any location and reviewed by any authorized user. In such a scenario the storage and transmission of medical records will have be conducted in a totally secure and safe environment with a very high standard of data integrity, protecting patient privacy and complying with all Health Insurance Portability and Accountability Act (HIPAA) regulations.

The large amount of broadcast and multimedia distribution channels introduces a new challenge for the advertisement industry. Keeping track of ad placements and effectiveness is highly necessary. This paper introduces a novel method for content monitoring and control through the use of video signatures. A video tomography based video signature is used. The goal is to determine the number of times a particular advt. clip is included in a video stream. The proposed method is low complexity and can monitor video streams in real-time. The proposed method not only allows the monitoring of the broadcast transmission, but also permits the extraction of user statistical behavior if this solution is included in setup boxes.

H.264/AVC encoder complexity is mainly due to variable size in Intra and Inter frames. This makes H.264/AVC very difficult to implement, especially for real time applications and mobile devices. The current technological challenge is to conserve the compression capacity and quality that H.264 offers but reduce the encoding time and, therefore, the processing complexity. This paper applies machine learning technique for video encoding mode decisions and investigates ways to improve the process of generating more general low complexity H.264/AVC video encoders. The proposed H.264 encoding method decreases the complexity in the mode decision inside the Inter frames. Results show, in a 67.81% average reduction of complexity and 0.2 average decreases in PSNR and an average bit rate increase of 0.04% for different kinds of videos and formats.

This paper proposes an improved Wyner-Ziv to H.264 video transcoder as part of a framework for mobile to mobile video communications. In this scheme, the user devices keep the low complexity constraints by using the Wyner-Ziv encoding and H.264 decoding algorithms. They shift their complexities to the network where the proposed transcoder is allocated. The main goal of the transcoder is to convert the bitstream and reduce the delay efficiently. The results show that the proposed transcoder reduces the complexity by a factor of 95% with a negligible rate-distortion loss.

Multi-view Video Coding (MVC) is showing a new demand in the video communications, video surveillance systems, video teleconferencing, TV communications, and 3D video games. New algorithms are needed to improve video compression and reduce the complexity of multiple views in order to improve the MVC systems. This paper presents the performance evaluation of the Hypercube Prediction Algorithm (HPA) for MVC. The main objective is to minimize the chain view of the dependencies based on the hypercube structure. Using spatio-temporal predictions based on the hypercube structure we show that MVC compression efficiency can be improved while keeping the dependencies low. The proposed HPA for MVC provides increased flexibility in selecting prediction references by reducing the view dependency by a factor of 2. The performance is compared with a Linear Prediction Algorithm (LPA) that uses one spatial and one temporal reference frame. We show that HPA can be used to allow flexible prediction structures that improve the encoding performance while keeping the sufficient the PSNR video quality.

The current technological advances allow for the easy distribution and duplication of video content through various venues, the most popular being the World Wide Web. Upholding copyright laws has thus become an important task; the result to this problem needs to be implemented in such a way to provide automatic video identification. This paper presents a novel approach to video identification based on the video tomography technique. The proposed video signature was designed and evaluated based on its ability to uniquely identify videos. A video signature should be both independent and robust. This paper focuses on evaluating the independence of the proposed video signatures. The signatures were evaluated using a large test set developed for the MPEG activity on digital video signatures. The results show that the proposed signature works well and exhibits strong independence necessary to support general purpose video identification systems.

In mobile-to video communications, both the sender and the receiver devices should not have higher complexity requirements to perform complex video compression tasks. The traditional video coding solutions are not suitable to support this communications due to its extremely complex encoding algorithm. On the other hand, the new Wyner-Ziv video coding paradigm reduces the complexity of the encoder at the expenses of a more complex decoder. In this paper, we propose an improved WZ/H.264 video transcoder to support this mobile-to-mobile communications, using the low complexity Wyner-Ziv encoding and the traditional H.264 decoding to be implemented in the end-user devices. The improved transcoder converts the video from the Wyner-Ziv to H.264 and reuses the motion vectors generated in the Wyner-Ziv decoding, in order to reduce the computational complexity of the motion estimation process in the H.264 encoding. Simulations results show a complexity reduction up to 55% with negligible rate-distortion drop.

Video identification or copy detection is a challenging problem and is becoming increasingly important with the growing popularity of online video services. The problem addressed in this paper is the identification of a given video clip in a given set of videos. For a given query video, the system returns all the instance of the video in the data set. This identification system uses video signatures based on video tomography. A robust and low complexity video signature is designed. The signatures are generated for video shots and not individual frames. This results in a compact signature of 64 bytes per video segment/ shot. The signatures are matched using simple Euclidean distance metric. Both signature generation and matching have a very low complexity. The system was evaluated using the dataset from video copy detection evaluations organized at the CIVR 2007. The results show that the proposed signatures outperform the results reported in the CIVR competition. The results also show that the signature is robust to common video transformations.

This paper describes complexity reduction in MPEG-2 to H.264 transcoding with resolution reduction. The methods developed are applicable to transcoding any DCT based video such as MPEG-2, MPEG-4, and H.263 to the recently standardized H.264 video at a reduced resolution. H.264 is being adopted by mobile device industry and devices such as iPod use H.264. The mobile devices, however, need the video at a reduced resolution. The proposed transcoder accelerates the H.264 encoding stage by performing motion estimation for only one block size as determined by the trained decision trees. Our solution allows conversion of MPEG-2 video to the H.264 video format at a reduced resolution with substantially less computing complexity. We use machine learning based approaches to significantly reduce the complexity of this transcoding. Experimental results show a reduction in transcoding time of about 67% (a 3x speedup) with a less than -0.5 dB change in PSNR.

This paper discusses techniques for efficiently implementing a Mpeg-2 to H.264 video transcoder. The transcoding results reported in the literature are based on a reference implementation and may not reflect the true performance gains obtained in real world systems. We have developed low complexity transcoding algorithms and have implemented these solutions using highly optimized encoder and decoder implementations available from Intel. The transcoding algorithms are based on exploiting the mode decision knowledge inherent in the decoded MPEG-2 data. Machine learning techniques are used to make accurate and low-complexity H.264 MB encoding mode decisions. The results show that the proposed transcoder reduces the complexity by 50% without a significant loss in PSNR. This performance improvement in production quality transcoders, and demonstrates the practicality of machine learning based video transcoding algorithms.