Novel Motion Anchoring Strategies for Wavelet-based Highly Scalable Video Compression

This thesis investigates new motion anchoring strategies that are targeted at wavelet-based highly scalable video compression (WSVC). We depart from two practices that are deeply ingrained in existing video compression systems. Instead of the commonly used block motion, which has poor scalability attributes, we employ piecewise-smooth motion together with a highly scalable motion boundary description. The combination of this more “physical” motion description together with motion discontinuity information allows us to change the conventional strategy of anchoring motion at target frames to anchoring motion at reference frames, which improves motion inference across time. In the proposed reference-based motion anchoring strategies, motion fields are mapped from reference to target frames, where they serve as prediction references; during this mapping process, disoccluded regions are readily discovered. Observing that motion discontinuities displace with foreground objects, we propose motion-discontinuity driven motion mapping operations that handle traditionally challenging regions around moving objects. The reference-based motion anchoring exposes an intricate connection between temporal frame interpolation (TFI) and video compression. When employed in a compression system, all anchoring strategies explored in this thesis perform TFI once all residual information is quantized to zero at a given temporal level. The interpolation performance is evaluated on both natural and synthetic sequences, where we show favourable comparisons with state-of-the-art TFI schemes. We explore three reference-based motion anchoring strategies. In the first one, the motion anchoring is “flipped” with respect to a hierarchical B-frame structure. We develop an analytical model to determine the weights of the different spatio-temporal subbands, and assess the suitability and benefits of this reference-based WSVC for (highly scalable) video compression. Reduced motion coding cost and improved frame prediction, especially around moving objects, result in improved rate-distortion performance compared to a target-based WSVC. As the thesis evolves, the motion anchoring is progressively simplified to one where all motion is anchored at one base frame; this central motion organization facilitates the incorporation of higher-order motion models, which improve the prediction performance in regions following motion with non-constant velocity

TEMPORAL REDUNDANCY REDUCTION IN WAVELET BASED VIDEO COMPRESSION FOR HIGH DEFINITION VIDEOS

Data Storage and Communication plays a significant role in every human. Digital images and videos are stored in mobile and other storage devices. More specifically, video data requires huge amount of storage space for which the storage devices are more expensive. Hence there is a necessity of reducing the storage space of the data. Video compression is more common in all researches. In this work, the role of wavelets in video compression is studied. The temporal redundant data are converted to spatial data which are then transformed to wavelet coefficients. The low frequency components are removed from these wavelet coefficients. The proposed method is tested with some video sequences. The performance of the proposed method is analyzed by comparing it with the existing recent methods and with the state-of-art H.265 video coding standard. The experimental results substantially proved that the proposed method achieves 3.8dB higher PSNR than H.265 and 1.6dB higher PSNR than recent wavelet based video codecs

Receiver-Driven Video Adaptation

In the span of a single generation, video technology has made an incredible impact on daily life. Modern use cases for video are wildly diverse, including teleconferencing, live streaming, virtual reality, home entertainment, social networking, surveillance, body cameras, cloud gaming, and autonomous driving. As these applications continue to grow more sophisticated and heterogeneous, a single representation of video data can no longer satisfy all receivers. Instead, the initial encoding must be adapted to each receiver's unique needs. Existing adaptation strategies are fundamentally flawed, however, because they discard the video's initial representation and force the content to be re-encoded from scratch. This process is computationally expensive, does not scale well with the number of videos produced, and throws away important information embedded in the initial encoding. Therefore, a compelling need exists for the development of new strategies that can adapt video content without fully re-encoding it. To better support the unique needs of smart receivers, diverse displays, and advanced applications, general-use video systems should produce and offer receivers a more flexible compressed representation that supports top-down adaptation strategies from an original, compressed-domain ground truth. This dissertation proposes an alternate model for video adaptation that addresses these challenges. The key idea is to treat the initial compressed representation of a video as the ground truth, and allow receivers to drive adaptation by dynamically selecting which subsets of the captured data to receive. In support of this model, three strategies for top-down, receiver-driven adaptation are proposed. First, a novel, content-agnostic entropy coding technique is implemented in which symbols are selectively dropped from an input abstract symbol stream based on their estimated probability distributions to hit a target bit rate. Receivers are able to guide the symbol dropping process by supplying the encoder with an appropriate rate controller algorithm that fits their application needs and available bandwidths. Next, a domain-specific adaptation strategy is implemented for H.265/HEVC coded video in which the prediction data from the original source is reused directly in the adapted stream, but the residual data is recomputed as directed by the receiver. By tracking the changes made to the residual, the encoder can compensate for decoder drift to achieve near-optimal rate-distortion performance. Finally, a fully receiver-driven strategy is proposed in which the syntax elements of a pre-coded video are cataloged and exposed directly to clients through an HTTP API. Instead of requesting the entire stream at once, clients identify the exact syntax elements they wish to receive using a carefully designed query language. Although an implementation of this concept is not provided, an initial analysis shows that such a system could save bandwidth and computation when used by certain targeted applications.Doctor of Philosoph

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Measurements, Models, Systems and Design

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CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches