Enhancing error resilience in wireless transmitted compressed video sequences through a probabilistic neural network core

Video compression standards commonly employed in the delivery of real-time wireless multimedia services regularly adopt variable length codes (VLCs) for efficient transmission. This coding technique achieves the necessary high compression ratios at the expense of an increased system’s vulnerability to transmission errors. The more frequent presence of transmission errors in wireless channels requires video compression standards to accurately detect, localize and conceal any corrupted macroblocks (MBs) present in the video sequence. Unfortunately, standard decoders offer limited error detection and localization capabilities posing a bound on the perceived video quality of the reconstructed video sequence. This paper presents a novel solution which enhances the error detection and localization capabilities of standard decoders through the application of a Probabilistic Neural Network (PNN). The proposed solution generally outperforms other error detection mechanisms present in literature, as it manages to improve the standard decoder’s error detection rate by up to 95.74%. Index Terms — Error detection coding, learning systems, multimedia communications, video coding, wireless networks.peer-reviewe

Fast inter-mode decision in multi-view video plus depth coding

Motion and disparity estimations are employed in Multi-view Video Coding (MVC) to remove redundancies present between temporal and different viewpoint frames, respectively, in both the color and the depth multi-view videos. These constitute the major computational expensive tasks of the video encoder, as iterative search for the optimal mode and its appropriate compensation vectors is employed to reduce the Rate-Distortion Optimization (RDO) cost function. This paper proposes a solution to limit the number of modes that are tested for RDO to encode the inter-view predicted views. The decision is based on the encoded information obtained from the corresponding Macroblock in the Base view, identified accurately by using the multi-view geometry together with the depth data. Results show that this geometric technique manages to reduce about 70% of the estimation's computational time and can also be used with fast geometric estimations to reduce up to 95% of the original encoding time. These gains are obtained with little degradation on the multi-view video quality for both color and depth MVC.peer-reviewe

Exploiting depth information for fast multi-view video coding

This research work is partially funded by the Strategic Educational Pathways Scholarship Scheme (STEPS-Malta). This scholarship is partly financed by the European Union – European Social Fund (ESF 1.25).Multi-view video coding exploits inter-view redundancies to compress the video streams and their associated depth information. These techniques utilize disparity estimation techniques to obtain disparity vectors (DVs) across different views. However, these methods contribute to the majority of the computational power needed for multi-view video encoding. This paper proposes a solution for fast disparity estimation based on multi-view geometry and depth information. A DV predictor is first calculated followed by an iterative or a fast search estimation process which finds the optimal DV in the search area dictated by the predictor. Simulation results demonstrate that this predictor is reliable enough to determine the area of the optimal DVs to allow a smaller search range. Furthermore, results show that the proposed approach achieves a speedup of 2.5 while still preserving the original rate-distortion performance.peer-reviewe

Efficient multiview depth representation based on image segmentation

The persistent improvements witnessed in multimedia production have considerably augmented users demand for immersive 3D systems. Expedient implementation of this technology however, entails the need for significant reduction in the amount of information required for representation. Depth image-based rendering algorithms have considerably reduced the number of images necessary for 3D scene reconstruction, nevertheless the compression of depth maps still poses several challenges due to the peculiar nature of the data. To this end, this paper proposes a novel depth representation methodology that exploits the intrinsic correlation present between colour intensity and depth images of a natural scene. A segmentation-based approach is implemented which decreases the amount of information necessary for transmission by a factor of 24 with respect to conventional JPEG algorithms whilst maintaining a quasi identical reconstruction quality of the 3D views.peer-reviewe

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices