JPWL: JPEG 2000 for Wireless Applications

In this paper, we present the current status of the JPWL standardization work item. JPWL is an extension of the JPEG 2000 baseline specification in order to enable the efficient transmission of JPEG 2000 codestream over an error-prone network. In particular, JPWL supports a set of tools and methods for error protection and correction such as Forward Error Correcting (FEC) codes, Unequal Error Protection (UEP), and data partitioning and interleaving. We then evaluate the performance of the JPWL Error Protection Block (EPB) tool. We consider two configurations of EPB: to protect the Main and Tile-part headers, or to protect the whole codestream using UEP. Experimental results show a significant quality improvement when using EPB compared to baseline JPEG 2000

JPWL - an Extension of JPEG 2000 for Wireless Imaging

In this paper, we present an overview of the JPWL standardization activity. JPWL is an extension of JPEG 2000 for the efficient transmission of JPEG 2000 images over an error-prone wireless network. More specifically, JPWL supports a set of tools for error protection and correction, including Forward Error Correcting codes (FEC), Unequal Error Protection (UEP), data partitioning and interleaving

Preserving data integrity of encoded medical images: the LAR compression framework

International audienceThrough the development of medical imaging systems and their integration into a complete information system, the need for advanced joint coding and network services becomes predominant. PACS (Picture Archiving and Communication System) aims to acquire, store and compress, retrieve, present and distribute medical images. These systems have to be accessible via the Internet or wireless channels. Thus protection processes against transmission errors have to be added to get a powerful joint source-channel coding tool. Moreover, these sensitive data require confidentiality and privacy for both archiving and transmission purposes, leading to use cryptography and data embedding solutions. This chapter introduces data integrity protection and developed dedicated tools of content protection and secure bitstream transmission for medical encoded image purposes. In particular, the LAR image coding method is defined together with advanced securization services

Wavelet based image compression integrating error protection via arithmetic coding with forbidden symbol and map metric sequential decoding with ARQ retransmission

The phenomenal growth of digital multimedia applications has forced the communication

Adaptive intra refresh for robust wireless multi-view video

This thesis was submitted for the award of PhD and was awarded by Brunel University LondonMobile wireless communication technology is a fast developing field and every day new mobile communication techniques and means are becoming available. In this thesis multi-view video (MVV) is also refers to as 3D video. Thus, the 3D video signals through wireless communication are shaping telecommunication industry and academia. However, wireless channels are prone to high level of bit and burst errors that largely deteriorate the quality of service (QoS). Noise along the wireless transmission path can introduce distortion or make a compressed bitstream lose vital information. The error caused by noise progressively spread to subsequent frames and among multiple views due to prediction. This error may compel the receiver to pause momentarily and wait for the subsequent INTRA picture to continue decoding. The pausing of video stream affects the user's Quality of Experience (QoE). Thus, an error resilience strategy is needed to protect the compressed bitstream against transmission errors. This thesis focuses on error resilience Adaptive Intra Refresh (AIR) technique. The AIR method is developed to make the compressed 3D video more robust to channel errors. The process involves periodic injection of Intra-coded macroblocks in a cyclic pattern using H.264/AVC standard. The algorithm takes into account individual features in each macroblock and the feedback information sent by the decoder about the channel condition in order to generate an MVV-AIR map. MVV-AIR map generation regulates the order of packets arrival and identifies the motion activities in each macroblock. Based on the level of motion activity contained in each macroblock, the MVV-AIR map classifies frames as high or low motion macroblocks. A proxy MVV-AIR transcoder is used to validate the efficiency of the generated MVV-AIR map. The MVV-AIR transcoding algorithm uses spatial and views downscaling scheme to convert from MVV to single view. Various experimental results indicate that the proposed error resilient MVV-AIR transcoder technique effectively improves the quality of reconstructed 3D video in wireless networks. A comparison of MVV-AIR transcoder algorithm with some traditional error resilience techniques demonstrates that MVV-AIR algorithm performs better in an error prone channel. Results of simulation revealed significant improvements in both objective and subjective qualities. No additional computational complexity emanates from the scheme while the QoS and QoE requirements are still fully met.Tertiary Institution Trust Fund (TETFund) of Nigeri

Utilizing Cross-Layer Information to Improve Performance in JPEG2000 Decoding

We focus on wireless multimedia communication and investigate how cross-layer information can be used to improve performance at the application layer, using JPEG2000 as an example. The cross-layer information is in the form of soft information from the physical layer. The soft information, which is supplied by a soft decision demodulator, yields reliability measures for the received bits and is fed into two soft input iterative JPEG2000 image decoders. When errors are detected with the error detecting mechanisms in JPEG2000, the decoders utilize the soft information to point out likely transmission errors. Hence, the decoders can correct errors and increase the image quality without making time-consuming retransmissions. We believe that the proposed decoding method utilizing soft information is suitable for a general IP-based network and that it keeps the principles of a layered structure of the protocol stack intact. Further, experimental results with images transmitted over a simulated wireless channel show that a simple decoding algorithm that utilizes soft information can give high gains in image quality compared to the standard hard-decision decoding

Implementing Efficient and Multi-Hop Image Acquisition In Remote Monitoring IoT systems using LoRa Technology

Remote sensing or monitoring through the deployment of wireless sensor networks (WSNs) is considered an economical and convenient manner in which to collect information without cumbersome human intervention. Unfortunately, due to challenging deployment conditions, such as large geographic area, and lack of electricity and network infrastructure, designing such wireless sensor networks for large-scale farms or forests is difficult and expensive. Many WSN-appropriate wireless technologies, such as Wi-Fi, Bluetooth, Zigbee and 6LoWPAN, have been widely adopted in remote sensing. The performance of these technologies, however, is not sufficient for use across large areas. Generally, as the geographical scope expands, more devices need to be employed to expand network coverage, so the number and cost of devices in wireless sensor networks will increase dramatically. Besides, this type of deployment usually not only has a high probability of failure and high transmission costs, but also imposes additional overhead on system management and maintenance. LoRa is an emerging physical layer standard for long range wireless communication. By utilizing chirp spread spectrum modulation, LoRa features a long communication range and broad signal coverage. At the same time, LoRa also has low power consumption. Thus, LoRa outperforms similar technologies in terms of hardware cost, power consumption and radio coverage. It is also considered to be one of the promising solutions for the future of the Internet of Things (IoT). As the research and development of LoRa are still in its early stages, it lacks sufficient support for multi-packet transport and complex deployment topologies. Therefore, LoRa is not able to further expand its network coverage and efficiently support big data transfers like other conventional technologies. Besides, due to the smaller payload and data rate in LoRa physical design, it is more challenging to implement these features in LoRa. These shortcomings limit the potential for LoRa to be used in more productive application scenarios. This thesis addresses the problem of multi-packet and multi-hop transmission using LoRa by proposing two novel protocols, namely Multi-Packet LoRa (MPLR) and Multi-Hop LoRa (MHLR). LoRa's ability to transmit large messages is first evaluated in this thesis, and then the protocols are well designed and implemented to enrich LoRa's possibilities in image transmission applications and multi-hop topologies. MPLR introduces a reliable transport mechanism for multi-packet sensory data, making its network not limited to the transmission of small sensor data only. In collaboration with a data channel reservation technique, MPLR is able to greatly mitigate data collisions caused by the increased transmission time in laboratory experiments. MHLR realizes efficient routing in LoRa multi-hop transmission by utilizing the power of machine learning. The results of both indoor and outdoor experiments show that the machine learning based routing is effective in wireless sensor networks

Multi-scale edge-guided image gap restoration

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.The focus of this research work is the estimation of gaps (missing blocks) in digital images. To progress the research two main issues were identified as (1) the appropriate domains for image gap restoration and (2) the methodologies for gap interpolation. Multi-scale transforms provide an appropriate framework for gap restoration. The main advantages are transformations into a set of frequency and scales and the ability to progressively reduce the size of the gap to one sample wide at the transform apex. Two types of multi-scale transform were considered for comparative evaluation; 2-dimensional (2D) discrete cosines (DCT) pyramid and 2D discrete wavelets (DWT). For image gap estimation, a family of conventional weighted interpolators and directional edge-guided interpolators are developed and evaluated. Two types of edges were considered; ‘local’ edges or textures and ‘global’ edges such as the boundaries between objects or within/across patterns in the image. For local edge, or texture, modelling a number of methods were explored which aim to reconstruct a set of gradients across the restored gap as those computed from the known neighbourhood. These differential gradients are estimated along the geometrical vertical, horizontal and cross directions for each pixel of the gap. The edge-guided interpolators aim to operate on distinct regions confined within edge lines. For global edge-guided interpolation, two main methods explored are Sobel and Canny detectors. The latter provides improved edge detection. The combination and integration of different multi-scale domains, local edge interpolators, global edge-guided interpolators and iterative estimation of edges provided a variety of configurations that were comparatively explored and evaluated. For evaluation a set of images commonly used in the literature work were employed together with simulated regular and random image gaps at a variety of loss rate. The performance measures used are the peak signal to noise ratio (PSNR) and structure similarity index (SSIM). The results obtained are better than the state of the art reported in the literature