Images account for a significant and growing fraction of Web downloads. The traditional approach to transporting images uses TCP, but this is overly restrictive for image data. Our analysis shows that the in-order delivery abstraction provided by a TCP-based approach prevents the receiver application from processing and rendering portions of an image when they actually arrive. Thus an image is rendered in bursts interspersed with long idle times rather than smoothly. This paper describes the design, implementation and evaluation of an image transport protocol (ITP) for image transmission over loss-prone congested or wireless networks. ITP improves user-perceived latency using application-level framing (ALF) and out-of-order application data unit (ADU) delivery, achieving significantly better interactive performance as measured by the evolution of peak signal-to-noise ratio (PSNR) with time at the receiver. ITP runs over UDP, incorporates receiver-driven selective reliability, uses a congestion manager (CM) to adapt to network congestion and is customizable for specific image formats (e.g., JPEG and JPEG2000). ITP enables a variety of new receiver post-processing algorithms such as error concealment that further improve the interactivity and responsiveness of reconstructed images. Performance experiments across a variety of loss conditions demonstrate the benefits of ITP in improving the interactivity of image downloads at the receiver.

Modeling the magnetic recording channel has long been a challenging research problem. Typically, the tradeoff has been simplicity of the model for its accuracy. For a given family of channel models, the accuracy will grow with the model size, at a price of a more complex model. We develop a formalism that strikes a balance between these opposing criteria. The formalism is based on Rissanen's (1978) notion of minimum required complexity-the minimum description length (MDL). The family of channel models in this study is the family of signal-dependent autoregressive channel models chosen for its simplicity of description and experimentally verified modeling accuracy. For this family of models, the minimum description complexity is directly linked to the minimum required complexity of a detector. Furthermore, the minimum description principle for autoregressive models lends itself for an intuitively pleasing interpretation. The description complexity is the sum of two terms: (1) the entropy of the sequence of uncorrelated Gaussian random variables driving the autoregressive filters, which decreases with the model order (i.e., model size), and (2) a penalty term proportional to the model size. We exploit this interpretation to formulate the minimum description length criterion for the magnetic recording channel corrupted by nonlinearities and signal-dependent noise. Results on synthetically generated data are presented to validate the method. We then apply the method to data collected from the spin stand to establish the model's size and parameters that strike a balance between complexity and accuracy.

Images account for a significant and growing fraction of Web downloads. The traditional approach to transporting images uses TCP, which provides a generic reliable, in-order byte-stream abstraction, but which is overly, restrictive for image data. We analyze the progression of image quality at the receiver with time and show that the in-order delivery abstraction provided by a TCP-based approach prevents the receiver application from processing and rendering portions of an image when they, actually, arrive. The end result is that an image is rendered in bursts interspersed with long idle times rather than smoothly. This paper describes the design, implementation, and evaluation of the Image Transport Protocol (ITP) for image transmission over loss-prone congested or wireless networks. ITP improves user-perceived latency using application level framing (ALF) and out-of-order pplication data unit (ADU) delivery achieving significantly better interactive performance as measured by the evolution of peak signal-to-noise ratio (PSNR) with time at the receiver ITP runs over UDP, incorporates receiver-driven selective reliability uses the congestion manager (CM) to adapt to network congestion, and is customizable for specific image formats (e.g., JPEG and JPEG2000). ITP enables a variety of new receiver post-processing algorithms such as error concealment that further improve the interactivity and responsiveness of reconstructed images. Performance experiments using our implementation across a variety of loss conditions demonstrate the benefits of ITP in improving the interactivity of image downloads at the receiver.

This paper considers the transmission of multiple descriptions over noisy channels rather than the on-off channels that are traditionally considered. We introduce the use of iterative decoding techniques similar to those used in "turbo" decoding to decode multiple correlated descriptions transmitted over a noisy channel. For a given transmission rate per channel and a given channel state, the efficacy of iterative decoding depends on the correlatedness of the two descriptions produced by the multiple description encoder. We demonstrate that there is an optimal amount of redundancy or correlation for a given channel state. Hence, multiple description codes may also be viewed as joint source-channel codes.

Multiple description coding (MDC) is a technique used to obtain two or more (often correlated) descriptions of a source, which are transmitted over different channels to the receiver. These descriptions of the source support two levels of reconstruction quality. When all the descriptions are received and used in the reconstruction, the source can be reconstructed with high fidelity. When only a subset of the descriptions is received, the quality of reconstruction should still be acceptable. In this work, we introduce MDC (with two descriptions) in the context of classification based subband coding. We develop an efficient MDC scheme using multiple description scalar quantizers and present a greedy algorithm to allocate rates and redundancies among the classified subbands.

This paper presents a general framework for combined source-channel coding within the context of subband coding. The unequal importance of subbands in reconstruction of the source is exploited by an appropriate allocation of source and channel coding rates for the coding and transmission of subbands over a noisy channel. For each subband, the source coding rate as well as the level of protection (quantified by the channel coding rate) are jointly chosen to minimize the total end-to-end mean-squared distortion suffered by the source. This allocation of source and channel coding rates is posed as a constrained optimization problem, and solved using a generalized bit allocation algorithm. The optimal choice of source and channel coding rates depends on the state of the physical channel. These results are extended to transmission over fading channels using a finite state model, where every state corresponds to an additive white Gaussian noise (AWGN) channel. A coding strategy is also developed that minimizes the average distortion when the channel state is unavailable at the transmitter. Experimental results are provided that demonstrate application of these combined source-channel coding strategies on video sequences.

This paper proposes a new adaptive source-channel coding scheme in the context of subband coding. We first express the total mean-squared distortion suffered by the source in terms of source and channel distortions of the subbands. We then minimize this total distortion by an appropriate choice of source and channel coding rates for the subbands. This corresponds to casting the conventional bit allocation problem in a joint source-channel coding context. The choice of rates depends on the state of the physical channel, modeled by a binary symmetric channel (BSC). We then use a finite state Markov model for a fading channel to generalize results obtained for the BSC. This results in a joint source-channel coding scheme that is optimized to the current state of a fading channel.

This paper proposes an adaptive source-channel subband coding scheme for the transmission of video over fading wireless channels. A three-dimensional subband decomposition followed by vector quantization of the subband coefficients forms the source coding strategy. For transmission over the channel, the individual subbands are offered different amounts of protection depending on their importance in reconstruction at the receiver. For each subband, the source coding rate as well as the level of protection (quantified by the channel coding rate) are jointly chosen to minimize the total mean-squared distortion suffered by the video coder. The choice of source and channel coding rates depends on the state of the physical channel. We use a finite state model for the fading channel, where every state corresponds to an AWGN channel. This results in a joint source-channel coding scheme that adapts in an optimal way to the current state of a fading channel.

The mixture model is an attempt to accurately model the ground truth in the case of low-resolution remote-sensed imagery. The model assumes that a pixel in the image does not consist of a single class, but consists instead of the sum of fractions of various classes. The authors use an iterative least squares approach to estimate these fractions for every pixel. Results are provided on synthetic data as well as real Advanced Very High Resolution Radiometer (AVHRR) data from the African continent.