Multiple Description Research Articles

HEVC compatible perceptual multiple description video coding for reliable video transmission over packet networks

Multiple description coding (MDC) has been an effective scheme for reliable transmission of videos over error prone networks but requires higher data rates. Recently, perceptual video coding schemes are able to encode videos at a lower data rate, but with the same perceived decoding quality. Such schemes exploit human visual system (HVS) properties to encode videos. Although HVS characteristics have been used in MDC, but only for residual information and are not fully standard compatible. To this end, we propose a high efficiency video coding (HEVC) standard compatible perceptual multiple description video coding (PMDVC) framework. Our proposed framework temporally sub-samples the input video into even and odd frame sub-sequences, which are individually encoded (using HEVC) in a perceptual manner. We encode each description using a context based visual saliency model to obtain visual saliency mask, which is optimally thresholded into salient and non-salient regions. Coding tree unit (CTU) level perceptual relevance mask of each frame is generated by dividing binary saliency mask into five different groups. The quantization parameter at CTU level of each perceptual relevant group is adjusted in such a manner that data rate is minimized while maintaining the perceived quality. Our proposed HEVC based PMDVC scheme is evaluated under lossless and lossy channel conditions. Our results show better performance in data rate reduction, perceptual peak signal-to-noise-ratio, multi- scale structural similarity index, and difference mean opinion score, when compared with HEVC based temporally sub-sampled multiple description video coding scheme.

A weakly secure multiple description coding scheme in lossy multipath channels for fine-grained SVC streaming

Video streaming service is growing rapidly these years. The quality of video streaming may suffer from issues like packet loss and solutions like multiple description coding (MDC) has been shown to provide flexible and distortion-rate optimal video streaming transmission over packet-loss links. On the other hand, the contents of stream are prone to attacks like eavesdropping while MDC is not specifically designed for security purpose. In this paper, fine grain scalably coded video data is channel-coded through multiple description-forward error correction (MD-FEC) coding. The resulting descriptions (packets) are then sent to a variety of channels, which may be peeked by an eavesdropper with certain probability. We adopt the weakly secure design here to reduce the quality of video obtained by the eavesdropper. Two weak security criteria for this problem are proposed, namely the deterministic overhearing peak-signal-to-noise-ratio (PSNR) constraint and the average overhearing PSNR constraint. The corresponding optimization algorithms are provided. Numerical results show that by applying weakly secure design, the PSNR value on the eavesdropper’s side will be reduced to the level at which useful details of the clip can hardly be exposed at the cost of reducing around 1–2 dB at the authorized receiver’s side.

Gestalt Psychotherapy and Coaching for Relationships

Gestalt Psychotherapy and Coaching for Relationships

Introducing Nurse-Led Patient Education Visits: Empowering Patients and Elevating Nursing Practice.

The literature includes multiple descriptions of successful nurse-led interventions, but the effects of nurse-led education on nurse and patient satisfaction in an executive health program are unknown. Nursing staff desire to practice more fully within their scope of licensure. Increased practice demands raised questions about whether nurse-led education would improve staff and patient satisfaction. A structured quality improvement process was used to design a nurse-led patient education program. Pilot measures included 5-point Likert scale patient and staff satisfaction surveys. Nurse burnout was also measured before and after the pilot. Patient satisfaction was high; 96% reported favorable satisfaction during the pilot, with sustained results over the following 3 years. Nurses' sense of achievement improved by 12 percentage points, and perception of making good use of skills and abilities increased by 39 percentage points. A nurse-led patient education intervention contributed to improved staff satisfaction while sustaining a positive patient experience.

Predictive side decoding for human-centered multiple description image coding

Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people’s varying watching situations as well as people’s demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra- and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.

Convolutional auto-encoder based multiple description coding network

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

Lattice-Based Robust Distributed Source Coding for Three Correlated Sources

The problem of robust distributed source coding for three correlated sources is studied in this work. A lattice-based scheme is proposed and the analysis of its performance is provided in the high resolution regime. Special attention is paid to the degenerate case where the three sources are Gaussian and identical. In this case, our scheme is shown to achieve within an asymptotic gap of 0.069 bits in terms of rate per description from the information-theoretic limit of quadratic symmetric Gaussian multiple description coding with central and individual decoders, when the side distortion and the ratio between the central and side distortions both approach 0.

Predictive Compositional Method to Design and Reoptimize Complex Behavioral Dataflows

In this article, we introduce an automatic stream computing reoptimization flow from ASICs to field-programmable gate arrays (FPGAs). Complex VLSI designs need to be prototyped and/or emulated on FPGAs. The main problem that we address in this article is that configurations optimized when targeting ASICs are often, as we will show in this article, highly un-optimal when remapped onto an FPGA. Thus, this article proposes a method to first generate a variety of dataflow configurations targeting an ASIC given multiple behavioral descriptions for high-level synthesis (HLS) and then, based on a compositional predictive model, automatically reoptimize the dataflow when mapped onto an FPGA. The experimental results show that our proposed method works well and that it is very fast.

On the Compound Broadcast Channel: Multiple Description Coding and Interference Decoding

This work investigates the general two-user Compound Broadcast Channel (BC) where an encoder wishes to transmit common and private messages to two receivers while being oblivious to two possible channel realizations controlling the communication. The focus is on the characterization of the largest achievable rate region by resorting to more evolved encoding and decoding techniques than the conventional coding for the standard BC. The role of the decoder is first explored, and an achievable rate region is derived based on the principle of "Interference Decoding" (ID) where each receiver decodes its intended message and chooses to (non-uniquely) decode or not the interfering message. This inner bound is shown to be capacity achieving for a class of non-trivial compound BEC/BSC broadcast channels while the worst-case of Marton's inner bound -based on "Non Interference Decoding" (NID)- fails to achieve the capacity region. The role of the encoder is then studied, and an achievable rate region is derived based on "Multiple Description" (MD) coding where the encoder transmits a common as well as multiple dedicated private descriptions to the many instances of the users channels. It turns out that MD coding outperforms the single description scheme -Common Description (CD) coding- for a class of compound Multiple Input Single Output Broadcast Channels (MISO BC).

A New Polyphase Down-Sampling Based Multiple Description Image Coding.

Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each N×N image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.

On the Compound Broadcast Channel: Multiple Description Coding and Interference Decoding

This work investigates the general two-user compound Broadcast Channel (BC) in which an encoder wishes to transmit two private messages W1 and W2 to two receivers while being oblivious to the actual channel realizations controlling the communication. The focus is on the characterization of the largest achievable rate region by resorting to more involved encoding and decoding techniques than the usual coding schemes of the standard BC. Involved decoding schemes are first explored, and an achievable rate region is derived based on the principle of Interference Decoding (ID), in which each receiver decodes its intended message and chooses to (non-uniquely) decode, or not, the interfering non-itended message. This decoding scheme is shown to be capacity achieving for a class of non-trivial compound BEC/BSC broadcast channels while the worst-case of Marton’s inner bound—based on No Interference Decoding (NID)—fails to achieve the capacity region. Involved encoding schemes are later investigated, and an achievable rate region is derived based on Multiple Description (MD) coding wherin the encoder transmits a common description as well as multiple dedicated private descriptions to the many possible channel realizations of the users. It turns out that MD coding yields larger inner bounds than the single description scheme—Common Description (CD) coding—for a class of compound Multiple Input Single Output Broadcast Channels (MISO BC).

Sutureless Laparoscopic Inguinal Hernia Repair in Children − Don’t Risk Injury with Sutures

Introduction: There have been multiple descriptions for the laparoscopic repair of inguinal hernia in children. Except for one, they all involve a suture ligation of the internal ring. Our concern is that suture ligation of the internal ring can injure the vas deferens, gonadal vessels, or cause unnecessary bleeding. We report the results of our laparoscopic inguinal hernia repairs, where we incise the peritoneum of the internal ring, and dissect the tunica vaginalis for 1 cm. We do not suture ligate the internal ring. This includes patients who underwent laparoscopic evaluation and treatment for intraabdominal testicles, and thus have 6 month laparoscopic follow-up of those patients having a staged orchiopexy. Study design: We reviewed the charts from 2005 to 2016 of all patients with CPT codes 54692 (laparoscopic orchiopexy) and 49650 (laparoscopic inguinal hernia). During laparoscopy for the nonpalpable testicle, we proceeded to either a one stage or 2 stage orchiopexy. When we observed inguinal hernia opening on the contralateral side of the undescended testicle, we repaired it by incising around the internal ring, and carefully excising the tunica vaginalis from the internal ring for a distance of 1 cm, leaving the internal oblique muscle to heal together, and peritoneum to resurface the area, without using sutures. (This is the same as is done for the laparoscopic orchiopexy side). In those patients with a staged orchiopexy, and we reevaluated the hernia sight for closure. Results: We reviewed charts of 87 patients with ages ranging from 3 months to 10 years. Eighteen of these had bilateral laparoscopic orchiopexy, 38 had left, and 24 had right. We performed 21 bilateral laparoscopic hernia repairs, 27 had left only, and 18 had right only. Of those patients with hernias, 11 hernias were found on the side opposite an undescended testis. In 4 patients we had the opportunity to perform a second stage orchiopexy six months later and observed the inguinal ring had closed. Follow up was at least 6 months for all patients, and no clinical hernias resulted. Conclusion: Laparoscopic inguinal hernia repair in children can be successfully and safely performed by excising the hernia sac (patent processus vaginalis) from the inguinal canal, and does not require suture ligation of the internal ring.

Cross-layer Optimization for Video Transmission using MDC in Wireless Mesh Networks

Abstract In this paper, we propose a cross-layer optimization technique for improving the QoS of video streaming applications in multi-radio Wireless Mesh Networks (WMNs) using Multiple Description Coding (MDC). WMN is an emerging technology which is used to connect various types of networks to the internet. As the trafficdensity is very high fromthe client mesh to the gateways, load balancing in these networks has become an important research issue. Thus, there is a need for multipath routing which can exploit the multi-radio capabilities along with the load balancing mechanism. Furthermore, the dynamic and shared nature of wireless medium makes the task of supporting QoS for video streaming applications challenging. Towards this,MDCis an effective technique for the video transmission over the unpredictable routes because of varying channel conditions.In this work,we compute multiple paths using Passive Interference andDelay Aware (PIDA) metric and performload balancing using MDC. Proposed work is implemented in Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol using NS2 simulator. The results reveal that proposed technique withMDCperforms better in terms of PSNR, frame delay and frame loss compared to existing technique

Teoria do posicionamento e terapia familiar: criando novas posições discursivas na relação entre mãe e filho

Seguindo a premissa Construcionista Social de que o indivíduo se constitui nas relações através da linguagem e partindo de um contexto prático do atendimento com uma família, o objetivo deste artigo é dar visibilidade ao processo de construção de novas posições discursivas para a relação de uma mãe e um filho, a partir do foco no processo de comunicação e do uso de uma metáfora. Teoricamente, nos apoiamos na compreensão do self em sua dimensão relacional. Metodologicamente, utilizamos a teoria do posicionamento como um recurso para dar visibilidade para o processo discursivo em terapia familiar, no qual uma mãe e um filho negociam posições alternativas para seu relacionamento. Concluímos, pela utilidade da teoria do posicionamento na experiência clínica, por permitir colocar em ação a ampliação da noção de identidade, substituindo o enfoque na unicidade pela possibilidade de múltiplas descrições de self.

Robust Baseband Compression Against Congestion in Packet-Based Fronthaul Networks Using Multiple Description Coding

In modern implementations of Cloud Radio Access Network (C-RAN), the fronthaul transport network will often be packet-based and it will have a multi-hop architecture built with general-purpose switches using network function virtualization (NFV) and software-defined networking (SDN). This paper studies the joint design of uplink radio and fronthaul transmission strategies for a C-RAN with a packet-based fronthaul network. To make an efficient use of multiple routes that carry fronthaul packets from remote radio heads (RRHs) to cloud, as an alternative to more conventional packet-based multiroute reception or coding, a multiple description coding (MDC) strategy is introduced that operates directly at the level of baseband signals. MDC ensures an improved quality of the signal received at the cloud in conditions of low network congestion, i.e., when more fronthaul packets are received within a tolerated deadline. The advantages of the proposed MDC approach as compared to the traditional path diversity scheme are validated via extensive numerical results.

High Bit-Depth Image Acquisition Framework Using Embedded Quantization Bias

In this paper, we present a novel image acquisition framework capable of reconstructing high bit-depth images using an array of low bit-depth scalar quantizers. Our key contribution is a codesign of pixel quantization and reconstruction in image acquisition pipeline. Different from the traditional image acquisition scheme, where each pixel value is quantized and reconstructed independently, the proposed framework is designed based on the interpixel correlations in local image regions. The interpixel correlations imply that quantized pixel values of adjacent locations can be interpreted as multiple descriptions of a common pixel value. Because combining multiple descriptions leads to reduced uncertainty, a pixel value with higher bit depth can be reconstructed by exploiting the interpixel correlations. In this paper, we propose to inject an embedded quantization bias (EQB) to the prequantized image signal and feed the sum to scalar quantizers. Injecting EQB has the same effect as shifting the quantizers by different amounts at different pixel locations, driving the quantized values of adjacent pixels to form informative descriptions of a common pixel value. We present comprehensive studies on this framework, including the optimal design of the EQB signal, the reconstruction strategies for noisy input, and generalized assumptions on interpixel correlations. We show in the experiments that the proposed image acquisition framework significantly outperforms the competing methods and effectively improves the quality of the reconstructed images.

Zero-Delay Multiple Descriptions of Stationary Scalar Gauss-Markov Sources

In this paper, we introduce the zero-delay multiple-description problem, where an encoder constructs two descriptions and the decoders receive a subset of these descriptions. The encoder and decoders are causal and operate under the restriction of zero delay, which implies that at each time instance, the encoder must generate codewords that can be decoded by the decoders using only the current and past codewords. For the case of discrete-time stationary scalar Gauss—Markov sources and quadratic distortion constraints, we present information-theoretic lower bounds on the average sum-rate in terms of the directed and mutual information rate between the source and the decoder reproductions. Furthermore, we show that the optimum test channel is in this case Gaussian, and it can be realized by a feedback coding scheme that utilizes prediction and correlated Gaussian noises. Operational achievable results are considered in the high-rate scenario using a simple differential pulse code modulation scheme with staggered quantizers. Using this scheme, we achieve operational rates within // of the theoretical lower bounds for varying description rates.

Leveraging Micro-Stories to Build Engagement, Inclusion, and Neural Networking in Immunology Education.

Storytelling is a highly effective strategy for delivering course content. It can provide real-world contexts and the relevance students desire. Through personal connections to the narrative details, anecdotes facilitate the incorporation of content into pre-existing knowledge and neural networks that enhances retention. In addition, stories can honor students' diverse backgrounds, which builds a sense of belonging and community. In turn, these aspects can drive intrinsic motivation to learn and increase students' alertness in class and overall engagement in the course. Despite the educational power of stories, there often is not enough time to integrate them into curricula. To address this dilemma, faculty can condense stories into micro-stories that require relatively minimal class time. Many aspects of stories that enhance learning can be leveraged in just a few sentences by focusing on narrative details that engage a variety of cognitive and emotional processes. In particular, the inclusion of multiple sensory descriptions and small details, like locations and names, can provide sufficient context to maintain the value stories provide. Micro-stories can function independently or extend a single theme throughout a course. Presented in this Perspective are examples of micro-stories for concepts in immunology and strategies for developing them. Proposals are made for leveraging micro-stories to enhance student engagement and course community, content retention and retrieval, and satisfaction with immunology courses of all sizes and levels.

Codebooks from generalized bent [formula omitted]-valued quadratic forms

Codebooks with small inner-product correlation have applications in unitary space–time modulations, multiple description coding over erasure channels, direct spread code division multiple access communications, compressed sensing, and coding theory. It is interesting to construct codebooks (asymptotically) achieving the Levenshtein bound. This paper presents a class of generalized bent Z4-valued quadratic forms, which contains functions proposed by Heng and Yue (2017). Using these generalized bent Z4-valued quadratic forms, we construct optimal codebooks achieving the Levenshtein bound. These codebooks have parameters (22m+2m,2m) and alphabet size 6.

Non-Asymptotic Converse Bounds and Refined Asymptotics for Two Source Coding Problems

In this paper, we revisit two multi-terminal lossy source coding problems: the lossy source coding problem with side information available at the encoder and one of the two decoders, which we term as the Kaspi problem (Kaspi, 1994), and the multiple description coding problem with one semi-deterministic distortion measure, which we refer to as the Fu-Yeung problem (Fu and Yeung, 2002). For the Kaspi problem, we first present the properties of optimal test channels. Subsequently, we generalize the notion of the distortion-tilted information density for the lossy source coding problem to the Kaspi problem and prove a non-asymptotic converse bound using the properties of optimal test channels and the well-defined distortion-tilted information density. Finally, for discrete memoryless sources, we derive refined asymptotics which includes the second-order, large, and moderate deviations asymptotics. In the converse proof of second-order asymptotics, we apply the Berry-Esseen theorem to the derived non-asymptotic converse bound. The achievability proof follows by first proving a type-covering lemma tailored to the Kaspi problem, then properly Taylor expanding the well-defined distortion-tilted information densities and finally applying the Berry-Esseen theorem. We then generalize the methods used in the Kaspi problem to the Fu-Yeung problem. As a result, we obtain the properties of optimal test channels for the minimum sum-rate function, a non-asymptotic converse bound and refined asymptotics for discrete memoryless sources. Since the successive refinement problem is a special case of the Fu-Yeung problem, as a by-product, we obtain a non-asymptotic converse bound for the successive refinement problem, which is a strict generalization of the non-asymptotic converse bound for successively refinable sources (Zhou, Tan, and Motani, 2017).