Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Complexity management of H.264/AVC video compression.

The H. 264/AVC video coding standard offers significantly improved compression efficiency and flexibility compared to previous standards. However, the high computational complexity of H. 264/AVC is a problem for codecs running on low-power hand held devices and general purpose computers. This thesis presents new techniques to reduce, control and manage the computational complexity of an H. 264/AVC codec. A new complexity reduction algorithm for H. 264/AVC is developed. This algorithm predicts "skipped" macroblocks prior to motion estimation by estimating a Lagrange ratedistortion cost function. Complexity savings are achieved by not processing the macroblocks that are predicted as "skipped". The Lagrange multiplier is adaptively modelled as a function of the quantisation parameter and video sequence statistics. Simulation results show that this algorithm achieves significant complexity savings with a negligible loss in rate-distortion performance. The complexity reduction algorithm is further developed to achieve complexity-scalable control of the encoding process. The Lagrangian cost estimation is extended to incorporate computational complexity. A target level of complexity is maintained by using a feedback algorithm to update the Lagrange multiplier associated with complexity. Results indicate that scalable complexity control of the encoding process can be achieved whilst maintaining near optimal complexity-rate-distortion performance. A complexity management framework is proposed for maximising the perceptual quality of coded video in a real-time processing-power constrained environment. A real-time frame-level control algorithm and a per-frame complexity control algorithm are combined in order to manage the encoding process such that a high frame rate is maintained without significantly losing frame quality. Subjective evaluations show that the managed complexity approach results in higher perceptual quality compared to a reference encoder that drops frames in computationally constrained situations. These novel algorithms are likely to be useful in implementing real-time H. 264/AVC standard encoders in computationally constrained environments such as low-power mobile devices and general purpose computers

Scalable and network aware video coding for advanced communications over heterogeneous networks

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis work addresses the issues concerned with the provision of scalable video services over heterogeneous networks particularly with regards to dynamic adaptation and user’s acceptable quality of service. In order to provide and sustain an adaptive and network friendly multimedia communication service, a suite of techniques that achieved automatic scalability and adaptation are developed. These techniques are evaluated objectively and subjectively to assess the Quality of Service (QoS) provided to diverse users with variable constraints and dynamic resources. The research ensured the consideration of various levels of user acceptable QoS The techniques are further evaluated with view to establish their performance against state of the art scalable and non-scalable techniques. To further improve the adaptability of the designed techniques, several experiments and real time simulations are conducted with the aim of determining the optimum performance with various coding parameters and scenarios. The coding parameters and scenarios are evaluated and analyzed to determine their performance using various types of video content and formats. Several algorithms are developed to provide a dynamic adaptation of coding tools and parameters to specific video content type, format and bandwidth of transmission. Due to the nature of heterogeneous networks where channel conditions, terminals, users capabilities and preferences etc are unpredictably changing, hence limiting the adaptability of a specific technique adopted, a Dynamic Scalability Decision Making Algorithm (SADMA) is developed. The algorithm autonomously selects one of the designed scalability techniques basing its decision on the monitored and reported channel conditions. Experiments were conducted using a purpose-built heterogeneous network simulator and the network-aware selection of the scalability techniques is based on real time simulation results. A technique with a minimum delay, low bit-rate, low frame rate and low quality is adopted as a reactive measure to a predicted bad channel condition. If the use of the techniques is not favoured due to deteriorating channel conditions reported, a reduced layered stream or base layer is used. If the network status does not allow the use of the base layer, then the stream uses parameter identifiers with high efficiency to improve the scalability and adaptation of the video service. To further improve the flexibility and efficiency of the algorithm, a dynamic de-blocking filter and lambda value selection are analyzed and introduced in the algorithm. Various methods, interfaces and algorithms are defined for transcoding from one technique to another and extracting sub-streams when the network conditions do not allow for the transmission of the entire bit-stream

DeepWiVe: deep-learning-aided wireless video transmission

We present DeepWiVe , the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform. Our DNN decoder predicts residuals without distortion feedback, which improves the video quality by accounting for occlusion/disocclusion and camera movements. We simultaneously train different bandwidth allocation networks for the frames to allow variable bandwidth transmission. Then, we train a bandwidth allocation network using reinforcement learning (RL) that optimizes the allocation of limited available channel bandwidth among video frames to maximize the overall visual quality. Our results show that DeepWiVe can overcome the cliff-effect , which is prevalent in conventional separation-based digital communication schemes, and achieve graceful degradation with the mismatch between the estimated and actual channel qualities. DeepWiVe outperforms H.264 video compression followed by low-density parity check (LDPC) codes in all channel conditions by up to 0.0485 in terms of the multi-scale structural similarity index measure (MS-SSIM), and H.265+ LDPC by up to 0.0069 on average. We also illustrate the importance of optimizing bandwidth allocation in JSCC video transmission by showing that our optimal bandwidth allocation policy is superior to uniform allocation as well as a heuristic policy benchmark

Optimized algorithms for multimedia streaming

Ph.DDOCTOR OF PHILOSOPH