High Dynamic Range Images Coding: Embedded and Multiple Description

The aim of this work is to highlight and discuss a new paradigm for representing high-dynamic range (HDR) images that can be used for both its coding and describing its multimedia content. In particular, the new approach defines a new representation domain that, conversely from the classical compressed one, enables to identify and exploit content metadata. Information related to content are used here to control both the encoding and the decoding process and are directly embedded in the compressed data stream. Firstly, thanks to the proposed solution, the content description can be quickly accessed without the need of fully decoding the compressed stream. This fact ensures a significant improvement in the performance of search and retrieval systems, such as for semantic browsing of image databases. Then, other potential benefits can be envisaged especially in the field of management and distribution of multimedia content, because the direct embedding of content metadata preserves the consistency between content stream and content description without the need of other external frameworks, such as MPEG-21. The paradigm proposed here may also be shifted to Multiple description coding, where different representations of the HDR image can be generated accordingly to its content. The advantages provided by the new proposed method are visible at different levels, i.e. when evaluating the redundancy reduction. Moreover, the descriptors extracted from the compressed data stream could be actively used in complex applications, such as fast retrieval of similar images from huge databases

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

An Optimal Video-Surveillance Approach for HDR Videos Tone Mapping

Recently, a new type of camera, which allows the recording of almost the entire range of luminosity of the acquired scene, has been introduced to the market. The produced High Dynamic Range images (HDRi), can simplify some video-surveillance tasks, such as object detection, recognition an tracking, since they can simultaneously capture the visual scene content of both dark and bright areas. Applications where lighting conditions cannot be controlled can greatly benet from the adoption of HDR cameras. For example, the achievement of a good and constant visual quality of images is one of the key aspects in video-surveillance applications, because it allows to robustly detect salient events in the captured scenes. However, the use of HDR images in traditional video-surveillance systems requires the reduction of their dynamic range appropriately, by applying a tone mapping operator. In this paper a fast method for tone mapping HDR videos, which combines the benets of both local and global operators, is presented. It is applied in the context of object detection and tracking. It also enhances the visual quality of the image in all light conditions, which facilitated surveillance tasks for both human and automatic operators