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Subcarrier assignment and power allocation for device-to-device (D2D) video transmission using a filter bank multicarrier waveform in a Rayleigh fading environment are investigated. We analyze the co-channel interference between D2D pairs, and propose a cross-layer algorithm with a subcarrier assignment outer loop and a power allocation inner loop, which aims to optimize the overall video quality. Unlike the non-convexity in physical layer power allocation for maximizing the total throughput, the cross-layer power allocation problem is convex under certain conditions, so a high quality solution for power allocation can be efficiently found. Simulation results demonstrate a higher overall video quality by the proposed cross-layer algorithm compared with baseline algorithms.

We consider an uplink multicarrier system with multiple video users who want to send compressed video data to the base station. In the time domain, we model the time-varying channel using Jakes' model, and in the frequency domain, each subcarrier is assumed to be independently fading. The video is scalably coded in units of a group of pictures (GOP), and users have different video rate distortion (RD) functions. At the beginning of the GOP, the base station collects both the RD information and the instantaneous channel state information (CSI) for subcarrier allocation purposes. We design a cross-layer resource allocation algorithm to assign subcarriers to users based on both the demand of the video and the quality of the channel. Once the resource allocation decision is made, the users then periodically adapt the modulation format of the subcarriers allocated according to the evolution of the CSI for the duration of the GOP. We show that our cross-layer resource allocation robustly outperforms two baseline algorithms, each of which uses only one layer of information for resource allocation.

We consider the joint source-channel coding problem of stereo video transmitted over AWGN and flat Rayleigh fading channels. Multiview coding (MVC) is used to encode the source, as well as a type of spatial scalable MVC. Our goal is to minimize the total number of bits, which is the sum of the number of source bits and the number of forward error correction bits, under the constraints that the quality of the left and right views must each be greater than predetermined PSNR thresholds at the receiver. We first consider symmetric coding, for which the quality thresholds are equal. Following binocular suppression theory, we also consider asymmetric coding, for which the quality thresholds are unequal. The optimization problem is solved using both equal error protection (EEP) and a proposed unequal error protection (UEP) scheme. An estimate of the expected end-to-end distortion of the two views is formulated for a packetized MVC bitstream over a noisy channel. The UEP algorithm uses these estimates for packet rate allocation. Results for various scenarios, including non-scalable/scalable MVC, symmetric/asymmetric coding, and UEP/EEP, are provided for both AWGN and flat Rayleigh fading channels. The UEP bit savings compared to EEP are given, and the performances of different scenarios are compared for a set of stereo video sequences.

We consider the joint source-channel coding (JSCC) problem of 3-D stereo video transmission in video plus depth format over noisy channels. Full resolution and downsampled depth maps are considered. The proposed JSCC scheme yields the optimum color and depth quantization parameters as well as the optimum forward error correction code rates used for unequal error protection (UEP) at the packet level. Different coding scenarios are compared and the UEP gain over equal error protection is quantified for flat Rayleigh fading channels. © 2014 IEEE.

© 2014 Springer Science+Business Media New York The cumulative mean squared error (CMSE) is a widely used measure of distortion introduced by a slice loss. We propose a low-complexity and low-delay generalized linear model for predicting CMSE contributed by the loss of individual H.264/AVC encoded video slices. We train the model over a video database by using a combination of video factors that are extracted during the encoding of the current frame, without using any data from future frames in the group of pictures (GOP). We then analyze the accuracy of the CMSE prediction model using cross-validation and correlation coefficients. We prioritize the slices within a GOP based on their predicted CMSE values. The performance of our model is evaluated by applying unequal error protection, using rate compatible punctured convolutional codes, to the prioritized slices over noisy channels. We also demonstrate an application of our slice prioritization by implementing a slice discard scheme, where the slices are dropped from the router when the network experiences congestion. The simulation results show that (i) the slice CMSE prediction model performs well for varying GOP structures, GOP lengths, and encoding bit rates, and (ii) the peak signal-to-noise ratio and video quality metric performance of an unequal error protection algorithm using slices prioritized by the predicted CMSE is similar to that of the measured CMSE values for different videos and channel signal-to-noise. We also extend the GOP-level slice prioritization to frame-level slice prioritization and show its performance over noisy channels.

We examine the performance of a cognitive radio system in a hostile environment where an intelligent adversary tries to disrupt communications by minimizing the system throughput. We investigate the optimal strategy for spoofing and jamming a cognitive radio network with a Gaussian noise signal over a Rayleigh fading channel. We analyze a cluster-based network of secondary users (SUs). The adversary may attack during the sensing interval to limit access for SUs by transmitting a spoofing signal. By jamming the network during the transmission interval, the adversary may reduce the rate of successful transmission. We present how the adversary can optimally allocate power across subcarriers during sensing and transmission intervals with knowledge of the system, using a simple optimization approach specific to this problem. We determine a worst-case optimal energy allocation for spoofing and jamming, which gives a lower bound to the overall information throughput of SUs under attack. © 1972-2012 IEEE.

How the human eye focuses for near; i.e. accommodates, is still being evaluated after more than 165 years. The mechanism of accommodation is essential for understanding the etiology and potential treatments for myopia, glaucoma and presbyopia. Presbyopia affects 100% of the population in the fifth decade of life. The lens is encased in a semi-elastic capsule with attached ligaments called zonules that mediate ciliary muscle forces to alter lens shape. The zonules are attached at the lens capsule equator. The fundamental issue is whether during accommodation all the zonules relax causing the central and peripheral lens surfaces to steepen, or the equatorial zonules are under increased tension while the anterior and posterior zonules relax causing the lens surface to peripherally flatten and centrally steepen while maintaining lens stability. Here we show with a balloon capsule zonular force model that increased equatorial zonular tension with relaxation of the anterior and posterior zonules replicates the topographical changes observed during in vivo rhesus and human accommodation of the lens capsule without lens stroma. The zonular forces required to simulate lens capsule configuration during in vivo accommodation are inconsistent with the general belief that all the zonules relax during accommodation., (© 2024. The Author(s).)

To determine the effect of zonular forces on lens capsule topography, a finite element (FE) analyses of lens capsules with no lens stroma and constant and variable thickness with anterior capsulotomies of 1.5 mm-6.5 mm were evaluated when subjected to equatorial (Ez), anterior (Az) and posterior (Pz) zonular forces. The lens capsule was considered in the unaccommodated state when the total initial zonular force was 0.00075 N or 0.3 N. From the total 0.00075 N zonular force, the Ez force was increased in 0.000125 N steps for a maximum force of 0.03 N and simultaneously the Az plus Pz force was reduced in 0.000125 N steps to zero. In addition, the force of all the zonules was reduced from 0.00075 N and separately from 0.3 N in 0.000125 N steps to zero. Only when Ez force was increased as Az and Pz force was reduced did the capsule topography simulate in vivo observations with the posterior capsule pole bowing posteriorly. The posterior bowing was directly related to Ez force and capsulotomy size. Whether the total force of all the zonules in the unaccommodated state was 0.00075 N or 0.3 N and reduced in steps to zero, the lens capsule topography did not emulate the in vivo observations. The FE analysis demonstrated that Ez tension increases while the Az and Pz tension decreases and that all the zonules do not relax during ciliary muscle contraction., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Some bias persiste d, but rubric use should be encouraged.

Due to limited transmission resources and storage capacity, efficient rate control is important in Video-based Point Cloud Compression (V-PCC). In this paper, we propose a learning-based rate control method to improve the rate-distortion (RD) performance of V-PCC. A low-latency synchronous rate control structure is designed to reduce the overhead of pre-coding. The basic unit (BU) parameters are predicted accurately based on our proposed CNN-LSTM neural network, instead of the online updating approach, which can be inaccurate due to low consistency between adjacent 2D frames in V-PCC. When determining the quantization parameters for the BU, a patch-based clipping method is proposed to avoid unnecessary clipping. This approach is able to improve the RD performance and subjective dynamic point cloud quality. Experiments show that our proposed rate control method outperforms present approaches.

Intra/inter switching-based error resilient video coding effectively enhances the robustness of video streaming when transmitting over error-prone networks. But it has a high computation complexity, due to the detailed end-to-end distortion prediction and brute-force search for rate-distortion optimization. In this article, a Low Complexity Mode Switching based Error Resilient Encoding (LC-MSERE) method is proposed to reduce the complexity of the encoder through a deep learning approach. By designing and training multi-scale information fusion-based convolutional neural networks (CNN), intra and inter mode coding unit (CU) partitions can be predicted by the networks rapidly and accurately, instead of using brute-force search and a large number of end-to-end distortion estimations. In the intra CU partition prediction, we propose a spatial multi-scale information fusion based CNN (SMIF-Intra). In this network a shortcut convolution architecture is designed to learn the multi-scale and multi-grained image information, which is correlated with the CU partition. In the inter CU partition, we propose a spatial-temporal multi-scale information fusion-based CNN (STMIF-Inter), in which a two-stream convolution architecture is designed to learn the spatial-temporal image texture and the distortion propagation among frames. With information from the image, and coding and transmission parameters, the networks are able to accurately predict CU partitions for both intra and inter coding tree units (CTUs). Experiments show that our approach significantly reduces computation time for error resilient video encoding with acceptable quality decrement.

When images and videos are displayed on a mobile device in bright ambient illumination, fewer details can be perceived than in the dark. The detail loss in dark areas of the images/videos is usually more severe. The reflected ambient light and the reduced sensitivity of viewer's eyes are the major factors. We propose two tone mapping operators to enhance the contrast and details in images/videos. One is content independent and thus can be applied to any image/video for the given device and the given ambient illumination. The other tone mapping operator uses simple statistics of the content. Display contrast and human visual adaptation are considered to construct the tone mapping operators. Both operators can be solved efficiently. Subjective tests and objective measurement show the improved quality achieved by the proposed methods.

Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions.

Underwater images often suffer from color distortion and low contrast, because light is scattered and absorbed when traveling through water. Such images with different color tones can be shot in various lighting conditions, making restoration and enhancement difficult. We propose a depth estimation method for underwater scenes based on image blurriness and light absorption, which can be used in the image formation model (IFM) to restore and enhance underwater images. Previous IFM-based image restoration methods estimate scene depth based on the dark channel prior or the maximum intensity prior. These are frequently invalidated by the lighting conditions in underwater images, leading to poor restoration results. The proposed method estimates underwater scene depth more accurately. Experimental results on restoring real and synthesized underwater images demonstrate that the proposed method outperforms other IFM-based underwater image restoration methods.

Quantitative analysis of cell shape in live samples is an important goal in developmental biology. Automated or semi-automated segmentation and tracking of cell nuclei has been successfully implemented in several biological systems. Segmentation and tracking of cell surfaces has been more challenging. Here, we present a new approach to tracking cell junctions in the developing epidermis of C. elegans embryos. Epithelial junctions as visualized with DLG-1::GFP form lines at the subapical circumference of differentiated epidermal cells and delineate changes in epidermal cell shape and position. We develop and compare two approaches for junction segmentation. For the first method (projection approach), 3-D cell boundaries are projected into 2D for segmentation using active contours with a nonintersecting force, and subsequently tracked using scale-invariant feature transform (SIFT) flow. The resulting 2-D tracked boundaries are then back-projected into 3-D space. The second method (volumetric approach) uses a 3-D extended version of active contours guided by SIFT flow in 3-D space. In both methods, cell junctions are manually located at the first time point and tracked in a fully automated way for the remainder of the video. Using these methods, we have generated the first quantitative description of ventral epidermal cell movements and shape changes during epidermal enclosure.

Wnt signals orient mitotic spindles in development, but it remains unclear how Wnt signaling is spatially controlled to achieve precise spindle orientation. Here, we show that C. elegans syndecan (SDN-1) is required for precise orientation of a mitotic spindle in response to a Wnt cue. We find that SDN-1 is the predominant heparan sulfate (HS) proteoglycan in the early C. elegans embryo, and that loss of HS biosynthesis or of the SDN-1 core protein results in misorientation of the spindle of the ABar blastomere. The ABar and EMS spindles both reorient in response to Wnt signals, but only ABar spindle reorientation is dependent on a new cell contact and on HS and SDN-1. SDN-1 transiently accumulates on the ABar surface as it contacts C, and is required for local concentration of Dishevelled (MIG-5) in the ABar cortex adjacent to C. These findings establish a new role for syndecan in Wnt-dependent spindle orientation., (© 2014. Published by The Company of Biologists Ltd.)

We consider a wireless relay network with a single source, a single destination, and a multiple relay. The relays are half-duplex and use the decode-and-forward protocol. The transmit source is a layered video bitstream, which can be partitioned into two layers, a base layer (BL) and an enhancement layer (EL), where the BL is more important than the EL in terms of the source distortion. The source broadcasts both layers to the relays and the destination using hierarchical 16-QAM. Each relay detects and transmits successfully decoded layers to the destination using either hierarchical 16-QAM or QPSK. The destination can thus receive multiple signals, each of which can include either only the BL or both the BL and the EL. We derive the optimal linear combining method at the destination, where the uncoded bit error rate is minimized. We also present a suboptimal combining method with a closed-form solution, which performs very close to the optimal. We use the proposed double-layer transmission scheme with our combining methods for transmitting layered video bitstreams. Numerical results show that the double-layer scheme can gain 2-2.5 dB in channel signal-to-noise ratio or 5-7 dB in video peak signal-to-noise ratio, compared with the classical single-layer scheme using conventional modulation.

We examine the visual effect of whole frame loss by different decoders. Whole frame losses are introduced in H.264/AVC compressed videos which are then decoded by two different decoders with different common concealment effects: frame copy and frame interpolation. The videos are seen by human observers who respond to each glitch they spot. We found that about 39% of whole frame losses of B frames are not observed by any of the subjects, and over 58% of the B frame losses are observed by 20% or fewer of the subjects. Using simple predictive features which can be calculated inside a network node with no access to the original video and no pixel level reconstruction of the frame, we developed models which can predict the visibility of whole B frame losses. The models are then used in a router to predict the visual impact of a frame loss and perform intelligent frame dropping to relieve network congestion. Dropping frames based on their visual scores proves superior to random dropping of B frames.

The original design of standard digital fountain codes assumes that the coded information symbols are equally important. In many applications, some source symbols are more important than others, and they must be recovered prior to the rest. Unequal Error Protection (UEP) designs are attractive solutions for such source transmissions. In this study, we introduce a more generalized design for the first universal fountain code design, LT codes, that makes it particularly suited for progressive bit stream transmissions. We apply the generalized LT codes to a progressive source and show that it has better UEP properties than other published results in the literature. For example, using the proposed generalization, we obtained up to 1.7dB PSNR gain in a progressive image transmission scenario over the two major UEP fountain code designs.

Multiple description coding has been receiving attention as a robust transmission framework for multimedia services. This paper studies the iterative decoding of FEC-based multiple description codes. The proposed decoding algorithms take advantage of the error detection capability of Reed-Solomon (RS) erasure codes. The information of correctly decoded RS codewords is exploited to enhance the error correction capability of the Viterbi algorithm at the next iteration of decoding. In the proposed algorithm, an intradescription interleaver is synergistically combined with the iterative decoder. The interleaver does not affect the performance of noniterative decoding but greatly enhances the performance when the system is iteratively decoded. We also address the optimal allocation of RS parity symbols for unequal error protection. For the optimal allocation in iterative decoding, we derive mathematical equations from which the probability distributions of description erasures can be generated in a simple way. The performance of the algorithm is evaluated over an orthogonal frequency-division multiplexing system. The results show that the performance of the multiple description codes is significantly enhanced.

A state-of-the-art progressive source encoder is combined with a concatenated block coding mechanism to produce a robust source transmission system for embedded bit streams. The proposed scheme efficiently trades off the available total bit budget between information bits and parity bits through efficient information block size adjustment, concatenated block coding, and random block interleavers. The objective is to create embedded codewords such that, for a particular information block, the necessary protection is obtained via multiple channel encodings, contrary to the conventional methods that use a single code rate per information block. This way, a more flexible protection scheme is obtained. The information block size and concatenated coding rates are judiciously chosen to maximize system performance, subject to a total bit budget. The set of codes is usually created by puncturing a low-rate mother code so that a single encoder-decoder pair is used. The proposed scheme is shown to effectively enlarge this code set by providing more protection levels than is possible using the code rate set directly. At the expense of complexity, average system performance is shown to be significantly better than that of several known comparison systems, particularly at higher channel bit error rates.

We improve the overall rate-distortion performance of distributed video coding by efficient techniques of correlation noise estimation and key frame encoding. In existing transform-domain Wyner-Ziv video coding methods, blocks within a frame are treated uniformly to estimate the correlation noise even though the success of generating side information is different for each block. We propose a method to estimate the correlation noise by differentiating blocks within a frame based on the accuracy of the side information. Simulation results show up to 2 dB improvement over conventional methods without increasing encoder complexity. Also, in traditional Wyner-Ziv video coding, the intercorrelation of key frames is not exploited since they are simply intracoded. In this paper, we propose a frequency band coding mode selection for key frames to exploit similarities between adjacent key frames at the decoder. Simulation results show significant improvement especially for low-motion and high frame rate sequences. Furthermore, the advantage of applying both schemes in a hierarchical order is investigated. This method achieves additional improvement.

Recently, multiple description source coding has emerged as an attractive framework for robust multimedia transmission over packet erasure channels. In this paper, we mathematically analyze the performance of n-channel symmetric FEC-based multiple description coding for a progressive mode of transmission over orthogonal frequency division multiplexing (OFDM) networks in a frequency-selective slowly-varying Rayleigh faded environment. We derive the expressions for the bounds of the throughput and distortion performance of the system in an explicit closed form, whereas the exact performance is given by an expression in the form of a single integration. Based on this analysis, the performance of the system can be numerically evaluated. Our results show that at high SNR, the multiple description encoder does not need to fine-tune the optimization parameters of the system due to the correlated nature of the subcarriers. It is also shown that, despite the bursty nature of the errors in a slow fading environment, FEC-based multiple description coding without temporal coding provides a greater advantage for smaller description sizes.

We consider the multiplexing problem of transmitting multiple video source streams from a server over a shared channel. We use dual-frame video coding with high-quality Long-Term Reference (LTR) frames and propose multiplexing methods to reduce the sum of mean squared error for all the video streams. This paper makes several improvements to dual-frame video coding. A simple motion activity detection algorithm is used to choose the location of LTR frames as well as the number of bits given to such frames. An adaptive buffer-constrained rate-control algorithm is devised to accommodate the extra bits of the high-quality LTR frames. Multiplexing of video streams is studied under the constraint of a video encoder delay buffer. Using H.264/AVC, the results show considerable improvement over baseline schemes such as H.264 rate control when the video streams are encoded individually and over multiplexing methods proposed previously in the literature. The high-quality LTR frames are offset in time among different video streams. This provides the benefits of dual-frame coding with high-quality LTR frames while still fitting under the constraint of an output delay buffer.

We consider the problem of simultaneous bitrate allocation for multiple video streams. Current methods for multiplexing video streams often rely on identifying the relative complexity of the video streams to improve the combined overall quality. In such methods, not all the videos benefit from the multiplexing process. Typically, the quality of high motion videos is improved at the expense of a reduction in the quality of low motion videos. In our approach, we use a competitive equilibrium allocation of bitrate to improve the quality of all the video streams by finding trades between videos across time. A central controller collects rate-distortion information from each video user and makes a joint bitrate allocation decision. Each user encodes and transmits his video at the allocated bitrate through a shared channel. The proposed method uses information about not only the differing complexity of the video streams at every moment but also the differing complexity of each stream over time. Using the competitive equilibrium bitrate allocation approach for multiple video streams, simulation results show that all the video streams perform better or at least as well as with individual encoding. The results of this research will be useful both for ad hoc networks that employ a cluster head model and for cellular architectures.

In this paper, we propose a generalized linear model for video packet loss visibility that is applicable to different group-of-picture structures. We develop the model using three subjective experiment data sets that span various encoding standards (H.264 and MPEG-2), group-of-picture structures, and decoder error concealment choices. We consider factors not only within a packet, but also in its vicinity, to account for possible temporal and spatial masking effects. We discover that the factors of scene cuts, camera motion, and reference distance are highly significant to the packet loss visibility. We apply our visibility model to packet prioritization for a video stream; when the network gets congested at an intermediate router, the router is able to decide which packets to drop such that visual quality of the video is minimally impacted. To show the effectiveness of our visibility model and its corresponding packet prioritization method, experiments are done to compare our perceptual-quality-based packet prioritization approach with existing Drop-Tail and Hint-Track-inspired cumulative-MSE-based prioritization methods. The result shows that our prioritization method produces videos of higher perceptual quality for different network conditions and group-of-picture structures. Our model was developed using data from high encoding-rate videos, and designed for high-quality video transported over a mostly reliable network; however, the experiments show the model is applicable to different encoding rates.

Coding and diversity are very effective techniques for improving transmission reliability in a mobile wireless environment. The use of diversity is particularly important for multimedia communications over fading channels. In this work, we study the transmission of progressive image bitstreams using channel coding in a 2-D time-frequency resource block in an OFDM network, employing time and frequency diversities simultaneously. In particular, in the frequency domain, based on the order of diversity and the correlation of individual subcarriers, we construct symmetric n -channel FEC-based multiple descriptions using channel erasure codes combined with embedded image coding. In the time domain, a concatenation of RCPC codes and CRC codes is employed to protect individual descriptions. We consider the physical channel conditions arising from various coherence bandwidths and coherence times, leading to a range of orders of diversities available in the time and frequency domains. We investigate the effects of different error patterns on the delivered image quality due to various fade rates. We also study the tradeoffs and compare the relative effectiveness associated with the use of erasure codes in the frequency domain and convolutional codes in the time domain under different physical environments. Both the effects of intercarrier interference and channel estimation errors are included in our study. Specifically, the effects of channel estimation errors, frequency selectivity and the rate of the channel variations are taken into consideration for the construction of the 2-D time-frequency block. We provide results showing the gain that the proposed model achieves compared to a system without temporal coding. In one example, for a system experiencing flat fading, low Doppler, and imperfect CSI, we find that the increase in PSNR compared to a system without time diversity is as much as 9.4 dB.

Motion-compensated fine-granularity scalability (MC-FGS) with leaky prediction has been shown to provide an efficient tradeoff between compression gain and error resilience, facilitating the transmission of video over dynamic channel conditions. In this paper, we propose an n-channel symmetric motion-compensated multiple description (MD) coding and transmission scheme for the delivery of scalable video over orthogonal frequency division multiplexed systems, utilizing the concepts of partial and leaky predictions. We investigate the proposed MD coding and transmission scheme using a cross-layer design perspective. In particular, we construct the symmetric motion-compensated MD codes based on the diversity order of the channel, defined as the ratio of the overall bandwidth of the system to the coherence bandwidth of the channel. We show that knowing the diversity order of a physical channel can assist an MC-FGS video coder in selecting the motion-compensation prediction point, as well as on the use of leaky prediction. More importantly, we illustrate how the side information can reduce the drift management problem associated with the construction of symmetric motion-compensated MD codes. We provide results based on both an information-theoretic approach and simulations.

Real-time video applications require tight bounds on end-to-end delay. Hierarchical bidirectional prediction requires buffering frames in the encoder input buffer, thereby contributing to encoder input delay. Long-term frame prediction with pulsed quality requires buffering at the encoder output, increasing the output buffer delay. Both hierarchical B-pictures and pulsed-quality coders involve uneven bit-rate allocation. Both the encoder and decoder buffering requirements depend on the rate allocation. We derive an efficient rate allocation for hierarchical B-pictures using the power spectral density of a wide-sense stationary process. In addition, we discuss important aspects of hierarchical predictive coding, such as the effect of the temporal prediction distance and delay tradeoffs for prediction branch truncation. Finally, we investigate experimentally the tradeoff between delay and compression efficiency.

Little attention has been paid to an impairment common in motion-compensated video compression: the addition of high-frequency (HF) energy as motion compensation displaces blocking artifacts off block boundaries. In this paper, we employ an energy-based approach to measure this motion-compensated edge artifact, using both compressed bitstream information and decoded pixels. We evaluate the performance of our proposed metric, along with several blocking and blurring metrics, on compressed video in two ways. First, ordinal scales are evaluated through a series of expectations that a good quality metric should satisfy: the objective evaluation. Then, the best performing metrics are subjectively evaluated. The same subjective data set is finally used to obtain interval scales to gain more insight. Experimental results show that we accurately estimate the percentage of the added HF energy in compressed video.

We present an adaptive video transmission scheme for use in a code-division multiple-access network, which incorporates efficient bandwidth allocation among source coding, channel coding, and spreading under a fixed total bandwidth constraint. We derive the statistics of the received signal, as well as a theoretical bound on the packet drop rate at the receiver. Based on these results, a bandwidth allocation algorithm is proposed at the packet level, which incorporates the effects of both the changing channel conditions and the dynamics of the source content. Detailed simulations are done to evaluate the performance of the system, and the sensitivity of the system to estimation error is presented.

We address the problem of enhancement layer drift estimation for fine granular scalable video. An optimal per-pixel drift estimation algorithm is introduced. The encoder assumes that there is some truncation of the enhancement layer, which does not allow the enhancement layer reference to be properly reconstructed, and the encoder recursively estimates the associated drift and chooses coding modes accordingly. The approach yields performance gains of about 1 dB across low to medium rates. In addition, we investigate dual frame prediction, for both base and enhancement layer, with pulsed-quality allocation in the base

Diversity can be used to combat multipath fading and improve the performance of wireless multimedia communication systems. In this work, by considering transmission of an embedded bitstream over an orthogonal frequency division multiplexing (OFDM) system in a slowly varying Rayleigh faded environment, we develop a cross-layer diversity technique which takes advantage of both multiple description coding and frequency diversity techniques. More specifically, assuming a frequency-selective channel, we study the packet loss behavior of an OFDM system and construct multiple independent descriptions using an FEC-based strategy. We provide some analysis of this cross-layer approach and demonstrate its superior performance using the set partitioning in hierarchical trees image coder.

A robust scheme is presented for the efficient transmission of packet video over a tandem wireless Internet channel. This channel is assumed to have bit errors (due to noise and fading on the wireless portion of the channel) and packet erasures (due to congestion on the wired portion). First, we propose an algorithm to optimally switch between intracoding and intercoding for a video coder that operates on a packet-switched network with fixed-length packets. Different re-synchronization schemes are considered and compared. This optimal mode selection algorithm is integrated with an efficient channel encoder, a cyclic redundancy check outer coder concatenated with an inner rate-compatible punctured convolutional coder. The system performance is both analyzed and simulated. Last, the framework is extended to operate on a time-varying wireless Internet channel with feedback information from the receiver. Both instantaneous feedback and delayed feedback are evaluated, and an improved method of refined distortion estimation for encoding is presented and simulated for the case of delayed feedback.

Video codecs that use motion compensation benefit greatly from the development of algorithms for near-optimal intra/inter mode switching within a rate-distortion framework. A separate development has involved the use of multiple-frame prediction, in which more than one past reference frame is available for motion estimation. In this paper, we show that using a dual-frame buffer (one short-term frame and one long-term frame available for prediction) together with intra/inter mode switching improves the compression performance of the coder. We improve the mode-switching algorithm with the use of half-pel motion vectors. In addition, we investigate the effect of feedback in making more informed and effective mode-switching decisions. Feedback information is used to limit drift errors due to packet losses by synchronizing the long-term frame buffers of both the encoder and the decoder.

With the growing importance of low-bandwidth applications, such as wireless access to the Internet, images are often sent or received at low bit rates. At these bit rates, they suffer from significant distortion and artifacts, making it difficult for those viewing the images to understand them. We present two progressive compression algorithms that focus on preserving the clarity of important image features, such as edges, at compression ratios of 80:1 and more. Both algorithms capture and encode the locations of important edges in the images. The first algorithm then transmits a standard SPIHT (set partitioning in hierarchical trees) bit stream, and at the decoder applies a nonlinear edge-enhancement procedure to improve the clarity of the encoded edges. The second approach uses a modified wavelet transform to "remove" the edges, and encodes the remaining texture information using SPIHT. With both approaches, features in the images that may be important for recognition are well preserved, even at low bit rates.

One method of transmitting wavelet based zerotree encoded images over noisy channels is to add channel coding without altering the source coder. A second method is to reorder the embedded zerotree bitstream into packets containing a small set of wavelet coefficient trees. We consider a hybrid mixture of these two approaches and demonstrate situations in which the hybrid image coder can outperform either of the two building block methods, namely on channels that can suffer packet losses as well as statistically varying bit errors.

We reanalyzed for covariability a set of 308 human immunodeficiency virus type 1 (HIV-1) V3 loop amino acid sequences from the B envelope sequence subtype previously analyzed by Korber et al.,1 as well as a new set of 440 sequences that also included substantial numbers of sequences from subtypes A, D, and E. We used the measure employed by Korber et al., essentially the likelihood ratio statistic for independence, plus two additional measures as well as clade information to examine the new set and both data sets simultaneously. We set forth the following conclusions and observations. The eight most highly connected sites identified through these statistical approaches included all of the six residues previously shown to have determining roles in structure, immunologic recognition, virus phenotype, and host range; each of the seven pairs of covariant sites found by Korber were signaled by our additional two measures in the set of 308 sequences, although 2 or 3 dropped out of the examination of the set of 440 when the requirement of stringent significance was applied for some or all of the three tests, respectively; using the same criteria, a total of 20 (including 5 Korber et al. pairs) or a total of 6 (including 4 Korber et al. pairs) were found when the set of 440 was added. Several limitations to statistical analysis of this type of HIV sequence data were also noted. For example, the data sets were, by historical necessity, collected haphazardly. For example, it was not possible to separate substantially sized groups out according to time of or since infection, disease status, antiviral treatment, geography, etc. There was also an enormous "wealth of significance" within the data. For example, for one measure the 440 data set showed 233 of the 465 pairs of sites with a likelihood ratio statistic of < 0.001. Last, most sites had consensus amino acids in 80% or more of the sequences; hence, there was an absence of data on many combinations of amino acids. Given the observed linkage between sites shown to be covariable and those known to have critical biological function, the statistical approaches we and Korber et al. have outlined may find use in predicting critical structural features of HIV proteins as targets for therapeutic intervention.

Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system. Vector quantization (VQ) provides a means of converting the decomposed signal into bits in a manner that takes advantage of remaining inter and intraband correlation as well as of the more flexible partitions of higher dimensional vector spaces. Since 1988, a growing body of research has examined the use of VQ for subband/wavelet transform coefficients. We present a survey of these methods.

Purpose: To evaluate the effects of lossy image (noninvertible) compression on diagnostic accuracy of thoracic computed tomographic images., Materials and Methods: Sixty images from patients with mediastinal adenopathy and pulmonary nodules were compressed to six different levels with tree-structured vector quantization. Three radiologists then used the original and compressed images for diagnosis. Unlike many previous receiver operating characteristic-based studies that used confidence rankings and binary detection tasks, this study examined the sensitivity and predictive value positive scores from nonbinary detection tasks., Results: At the 5% significance level, there was no statistically significant difference in diagnostic accuracy of image assessment at compression rates of up to 9:1., Conclusion: The techniques presented for evaluation of image quality do not depend on the specific compression algorithm and provide a useful approach to evaluation of the benefits of any lossy image processing technique.

The authors apply a lossy compression algorithm to medical images, and quantify the quality of the images by the diagnostic performance of radiologists, as well as by traditional signal-to-noise ratios and subjective ratings. The authors' study is unlike previous studies of the effects of lossy compression in that they consider nonbinary detection tasks, simulate actual diagnostic practice instead of using paired tests or confidence rankings, use statistical methods that are more appropriate for nonbinary clinical data than are the popular receiver operating characteristic curves, and use low-complexity predictive tree-structured vector quantization for compression rather than DCT-based transform codes combined with entropy coding. The authors' diagnostic tasks are the identification of nodules (tumors) in the lungs and lymphadenopathy in the mediastinum from computerized tomography (CT) chest scans. Radiologists read both uncompressed and lossy compressed versions of images. For the image modality, compression algorithm, and diagnostic tasks the authors consider, the original 12 bit per pixel (bpp) CT image can be compressed to between 1 bpp and 2 bpp with no significant changes in diagnostic accuracy. The techniques presented here for evaluating image quality do not depend on the specific compression algorithm and are useful new methods for evaluating the benefits of any lossy image processing technique.