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We are surrounded by a very dense and detailed environment. However, when we perceive, we are unable to attend to all the details at once. Certain fully visible but unexpected objects get overlooked when we become involved in other tasks. Change Blindness and In-attentional blindness are psychological phenomenon where a visual stimulus goes unnoticed by the observer. In this paper, we present results of the effect of subliminal cueing at a targeted area of a video which is prone to go unnoticed due to in-attentional blindness. This helps us in understanding human visual processing response and has various applications such as surveillance and driving safety.

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.

Nowadays it is very hard to find available spots in public parking lots and even harder in public facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This paper presents a system for automatic parking lot occupancy computation using motion tracking. Methods for complexity reduction are presented. The system showed approximately 96% accuracy in determining parking lot occupancy. We showed that by optimizing the resolution and bitrate of the input video, we can reduce the complexity by 70% and still achieved over 90% of accuracy. The results showed that high quality video is not necessary for the proposed algorithm to obtain accurate results.

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.

We present a method that employs human visual system characteristics for perceptually lossless optimization of video coding. Algorithm is robust and applicable for any modern hybrid coder. We decided to evaluate it using the most recent standard (HEVC/H.265) in order to show potential benefits for future applications. Experiments show savings of up to 8% in bitrate without any loss in quality as confirmed by two sets of subjective experiments using DSCQS and ACJ methodology.

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.

Video carving has become an essential tool in digital forensics. Video carving enables recovery of deleted video files from hard disks. Processing data to extract videos is a computationally intensive task. In this paper we present two methods to accelerate video carving: a method to accelerate fragment extraction, and a method to accelerate combining of these fragments into video segments. Simulation results show that complexity of video fragment extraction can be reduced by as much as 75% with minimal impact on the videos recovered.

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.

Current research towards 3D video compression within MPEG requires the compression of three texture and depth views. To reduce the additional complexity and bit rate of the depth map encoding, we present a fast mode decision model based on previously encoded macroblocks of the texture view. Meanwhile we present techniques to reduce the rate based on predicting syntax elements from the corresponding texture view. The proposed system is able to get a reduction in complexity of 71.08% with an average bit rate gain of 4.35%.

Cues from human visual system (HVS) can be used for further optimization of compression in modern hybrid video coding platforms. We present work that explores and exploits motion related attentional limitations. Algorithms for exploiting motion triggered attention were developed and compared with MPEG AVC/H.264 encoder with various settings for different bitrate levels. For the sequences with high motion activity our algorithm provides up to 8% bitrate savings.

In this paper we present a solution to improve the performance of adaptive HTTP streaming services. The proposed approach uses a content aware method to determine whether switching to a higher bitrate can improve video quality. The proposed solution can be implemented as a new parameter in segment description to enable content switching only in cases with meaningful increase in quality. Results of our experiments show clear advantages of using additional parameter in DASH implementation. The proposed approach enables significant bandwidth savings with minimal decrease in quality. It guarantees optimal path of adaptation in various scenarios that can be beneficial both for network providers and end users.

In this paper, a permutation scheme is introduced for Distributed Video Coding. The main goal is to preserve privacy and security in video surveillance video communications and can be adapted for other scenarios. The proposed approach consists to apply permutation based encryption to the DVC scenario. The permutation is defined by a secret key which is required at the decoder for decompression providing security. Simulation results show that the reference architecture and the permutation-based proposed have similar rate distortion performance.

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.

Today, world is rapidly turning to high definition multimedia. From engineering and programming point of view, this usually means more computation is needed and more memory space is required to achieve these higher qualities. In this paper we explore the use of parallelization opportunities in graphics processors to accelerate video encoding. We evaluate the NVIDIA CUDA[1] toolkit and evaluate the performance of motion estimation in video encoding. The main goal of this paper is to evaluate the capabilities of NVIDIA/CUDA and develop a process for implementing video/multimedia applications. We have discovered that the difference in performance when CUDA is not used properly can be over 100x. We show how we were able to use CUDA capabilities to reduce the motion estimation time from 7000 milli seconds to 70 milli seconds.

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.

In this paper we present a novel macroblock mode decision algorithm to speedup H.264/SVC Intra frame encoding. We replace the complex mode-decision calculations by a classifier which has been trained specifically to minimize the reduction in RD performance. This results in a significant speedup in encoding. The results show that machine learning has a great potential and can reduce the complexity substantially with negligible impact on quality. The results show that the proposed method reduces encoding time to about 70% in base layer and up to 50% in enhancement layer of the reference implementation with a negligible loss in quality.

In this chapter we describe our vision for the next generation of IT services. Services will be automatically discovered, procured and integrated with the service consumer's technical environment. This whole process will be determined by the policies defined by the consumer and will be transparent to the consumer. We also define the new applications of Policy Manager, Service Manager, Service Procurer and Service Integrator that will allow Smart Services to operate efficiently on the Smart Internet. Usability and User issues are also identified. We illustrate the Smart Services with examples from the health care and multimedia domains.

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.

Low power consumption in computing systems is a key requirement for devices such as cell phones and cameras. In this paper we present a low power DCT implementation using a highly scalable multiplier. This paper focuses on IDCT with playback applications such as digital photo displays. The proposed solution exploits the fact that the size of the multiplications varies per stage in a multistage IDCT implementation and configuring multipliers to match the needs of each stage saves power. Results are compared with Wallace and Array multipliers. We show that using a scalable multiplier and dynamically reconfiguring the width of the multiplier leads to significant power savings (over 72%) with negligible degradation in decoded image quality.

H.264/AVC encoder complexity is mainly due to variable block 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, on average, a 67.36% reduction in encoding time, a 0.2 dB decrease in PSNR, and an average bit rate increase of 0.05%.

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.

Wyner-Ziv (WZ) coding has been receiving attention from the research community as it offers a more symmetric complexity in video communication by reducing the complexity of video encoding. This encoding paradigm, however, increases the complexity of video decoding. This paper presents a novel video communication architecture that exploits the low complexity WZ encoding without being burdened by high complexity WZ decoding. At the core of the proposed architecture is a WZ to H.264 transcoder that converts WZ video to H.264 to reduce the amount of resources necessary for decoding. This architecture enables low complexity encoding and decoding on mobile devices. Using very simple WZ encoders and H.264 decoders, a communication system will only need very low complexity devices at both the sender and receiver sides. Such low complexity communication devices are highly desirable for practical consumer applications. The paper also presents the low complexity algorithms for WZ to H.264 transcoding. The time reduction of the improved WZ/H.264 transcoder offers a very acceptable 86% complexity reduction with negligible Rate-Distortion loss.

Video applications on handheld devices such as smart phones pose a significant challenge to achieve high quality user experience. Recent advances in processor and wireless networking technology are producing a new class of multimedia applications (e.g. video streaming) for mobile handheld devices. These devices are light weight and have modest sizes, and therefore very limited resources - lower processing power, smaller display resolution, lesser memory, and limited battery life as compared to desktop and laptop systems. Multimedia applications on the other hand have extensive processing requirements which make the mobile devices extremely resource hungry. In addition, the device specific properties (e.g. display screen) significantly influence the human perception of multimedia quality. In this paper we propose a saliency based framework that exploits the structure in content creation as well as the human vision system to find the salient points in the incoming bitstream and adapt it according to the target device, thus improving the quality of new adapted area around salient points. Our experimental results indicate that the adaptation process that is cognizant of video content and user preferences can produce better perceptual quality video for mobile devices. Furthermore, we demonstrated how such a framework can affect user experience on a handheld device.

Machine learning has been widely used in video analysis and search applications. In this paper, we describe a non-traditional use of machine learning in video processing - video encoding and transcoding. Video encoding and transcoding are computationally intensive processes and this complexity is increasing significantly with new compression standards such as H.264. Video encoders and transcoders have to manage the quality vs. complexity tradeoff carefully. Optimal encoding is prohibitively complex and sub-optimal coding decisions are usually used to reduce complexity but also sacrifices quality. Resource constrained devices cannot use all the advanced coding tools offered by the standards due to computational needs. We show that machine learning can be used to reduce the computational complexity of video coding and transcoding problems without significant loss in quality. We have developed the use of machine learning in video coding and transcoding and have evaluated it on several encoding and transcoding problems. We describe the general ideas in the application of machine learning and present the details of four different problems: 1) MPEG-2 to H.264 video transcoding, 2) H.263 to VP6 transcoding, 3) H.264 encoding and 4) Distributed Video Coding (DVC). Our results show that use of machine learning significantly reduces the complexity of encoders/transcoders and enables efficient video encoding on resource constrained devices such as mobile devices and video sensors.

One of the main concerns of the wide use of video surveillance is the loss of individual privacy. Individuals who are not suspects need not be identified on camera recordings. Mechanisms that protect the identity while ensuring legitimate security needs are necessary. Selectively encrypting objects that reveal identity (e.g., faces or vehicle tags) is necessary to preserve individualspsila right to privacy. This paper presents a compression algorithm independent solution that provides privacy in video surveillance applications. The proposed approach is based on the use of permutation based encryption to hide identity revealing features. The permutation based encryption tolerates lossy compression and allows decryption at a later time. The use of permutation based encryption makes the proposed solution independent of the compression algorithms used. The paper presents the performance of the system when using H.264 video encoding.

In this paper we present the effect of depth quality on 3D video perception on autostereoscopic displays. This study was done using objective as well as subjective evaluation (using Phillips monitor). The goal of this work was to understand the impact of depth image quality and compression on the perceived 3D experience. Another objective is to evaluate the use of depth image quality as a measure of 3D quality. The experiments were conducted using a multi-view point Philips 3D display that uses a single view and depth image. The displays are tested using subjective evaluation experiments. The results show that depth image can be compressed significantly, to 0.0007 bits per pixel, without affecting the 3D perception significantly. We found that there is a relation between the perception of depth, depth complexity and motion. Motion and complexity of the depth image have a strong influence on the acceptable depth quality in 3D videos. While depth quality gives useful information about 3D perception, additional work is necessary for developing objective metrics based on depth quality.

Networks-on-chip (NoC) are expected to play a key role in future embedded systems. A NoC-based system has the potential to support concurrent processing, in both software and hardware. This can however lead to concurrency issues. We present a multiprocessor system modeling and performance evaluation approach that addresses concurrency. We illustrate our methodology by mapping a H.264 decoder onto a 4 x 3 mesh- based NoC architecture. We show latency, area, and power consumption results for this NoC architecture abstracted from its FPGA implementation.

VP6 is a video coding standard developed by On2 Technologies. It is the preferred codec in the Flash 8/9 format used by many popular online video services and user generated content sites. The wide adoption of Flash video for video delivery on the Internet has made VP6 one of the most widely used video compression standards on the Internet. With the wide adoption of VP6 comes the need for transcoding other video formats to the VP6 format. This paper presents algorithms to transcode H.263 to the VP6 format. This transcoder has applications in media adaptation including converting older Flash video formats to Flash 8 format. The transcoding algorithms reuse the information from the H.263 decoding stage and accelerate the VP6 encoding stage. Experimental results show that the proposed algorithms are able to reduce the encoding complexity by up to 52% while reducing the PSNR by at most 0.42 dB in the worst case.

The goal of Multi-view Video Coding is to allow coding of multiple camera views such that the user has the freedom of choosing the view point. While there have been significant efforts in multi-view compression, the work on navigation has been limited. The biggest challenge here is in developing user friendly and intuitive navigation of multi-view content. We propose a system which consists of multi-view video player and a motion sensing remote controller. Motion sensing remote controllers became widely available with the introduction of the Nintendo Wii game console. The remote controller captures motion when waved and the motion information is used to change the views in multi view video applications. This system gives users a convenient way of navigating through the multiple views by waving the remote in the desired direction.

The H.264 video coding standard is highly efficient and at the same time highly complex. The complexity of encoding is substantially higher than prior standards such as H.263 and makes H.264 video encoding on mobile devices expensive. The few existing encoding solutions on mobile devices restrict the features used there by sacrificing quality for complexity. Fast encoding algorithms are necessary to fully exploit the advanced compression features offered by H.264. In this paper we present a novel machine learning based approach to reducing the complexity of intra MB coding. The results show that machine learning has a great potential and can reduce the complexity substantially with negligible impact on quality. The results show that the proposed method reduces encoding time to about 1/3 of the reference implementation with a negligible loss in quality.

Most of the reported distributed video coding (DVC) architectures have a serious limitation that hinders its practical application. The uses of a feedback channel between the encoder and the decoder require an interactive decoding procedure which is a limitation for applications such as offline processing. On the other hand, the decoder needs an efficient way to estimate the probability of error without assuming the availability of the original video at the decoder. In this paper we continue with our previous works into a more practical DVC architecture which solves both problems based on the use of machine learning. The proposed approach is based on extracting the relationships that exist between the residual frame and the number of requests over this feedback channel. We apply these concepts to pixel-domain Wyner-Ziv coding demonstrating significant savings in bitrates with a little loss of quality.