Unequal Error Protected JPEG 2000 Broadcast Scheme with Progressive Fountain Codes

This paper proposes a novel scheme, based on progressive fountain codes, for broadcasting JPEG 2000 multimedia. In such a broadcast scheme, progressive resolution levels of images/video have been unequally protected when transmitted using the proposed progressive fountain codes. With progressive fountain codes applied in the broadcast scheme, the resolutions of images (JPEG 2000) or videos (MJPEG 2000) received by different users can be automatically adaptive to their channel qualities, i.e. the users with good channel qualities are possible to receive the high resolution images/vedio while the users with bad channel qualities may receive low resolution images/vedio. Finally, the performance of the proposed scheme is evaluated with the MJPEG 2000 broadcast prototype

Algorithms & implementation of advanced video coding standards

Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage

Symbolic inductive bias for visually grounded learning of spoken language

A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.Comment: ACL 201

Perception-aware low-power audio processing techniques for portable devices

Ph.DDOCTOR OF PHILOSOPH

Representation Learning: A Review and New Perspectives

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning

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

A Survey on Semantic Communications for Intelligent Wireless Networks

With deployment of 6G technology, it is envisioned that competitive edge of wireless networks will be sustained and next decade's communication requirements will be stratified. Also 6G will aim to aid development of a human society which is ubiquitous and mobile, simultaneously providing solutions to key challenges such as, coverage, capacity, etc. In addition, 6G will focus on providing intelligent use-cases and applications using higher data-rates over mill-meter waves and Tera-Hertz frequency. However, at higher frequencies multiple non-desired phenomena such as atmospheric absorption, blocking, etc., occur which create a bottleneck owing to resource (spectrum and energy) scarcity. Hence, following same trend of making efforts towards reproducing at receiver, exact information which was sent by transmitter, will result in a never ending need for higher bandwidth. A possible solution to such a challenge lies in semantic communications which focuses on meaning (context) of received data as opposed to only reproducing correct transmitted data. This in turn will require less bandwidth, and will reduce bottleneck due to various undesired phenomenon. In this respect, current article presents a detailed survey on recent technological trends in regard to semantic communications for intelligent wireless networks. We focus on semantic communications architecture including model, and source and channel coding. Next, we detail cross-layer interaction, and various goal-oriented communication applications. We also present overall semantic communications trends in detail, and identify challenges which need timely solutions before practical implementation of semantic communications within 6G wireless technology. Our survey article is an attempt to significantly contribute towards initiating future research directions in area of semantic communications for intelligent 6G wireless networks