In mobile video applications, where unreliable networks are commonplace, corrupted video packets can have a profound impact on the quality of the user experience. In this paper, we show that, in a wide range of operating conditions, selectively reusing data resulting from decodable errorneous packets leads to better results than frame copy. This selection is guided by a novel concept that combines motion estimation and a measure of blocking artifacts at block edges to predict visual degradation caused by the decoding of erroneous packets. Simulation results show that, by using the proposed solution, the H.264/AVC JM reference software decoder can select the best option between frame copy and the erroneous frame decoding in 82% of test cases. We also obtain an average gain of 1.95 dB for concealed frames (when they differ from those concealed by the JM decoder).

The problem of adapting JPEG images to satisfy constraints such as file size and resolution arises in a number of applications, from universal media access to multimedia messaging services. Visually optimized adaptation, however, commands a non-negligible computational cost which we aim to minimize using predictors. In previous works, we presented predictors and systems to achieve low-cost near-optimal adaptation of JPEG images. In this work, we propose a new approach to file size and quality prediction resulting from the Transcoding of a JPEG image subject to changes in quality factor and resolution. We show that the new predictor significantly outperforms the previously proposed solutions in accuracy.

A common transcoding operation consists of reducing the file size of a JPEG image to meet bandwidth or device constraints. This can be achieved by reducing its quality factor (QF) or reducing its resolution, or both. In this paper, using the Structural SIMilarity (SSIM) index as the quality metric, we present a system capable of estimating the QF and scaling parameters to achieve optimal quality while meeting a device's constraints. We then propose a novel low-complexity JPEG transcoding system which delivers near-optimal quality. The system is capable of predicting the best combination of QF and scaling parameters for a wide range of device constraints and viewing conditions. Although its computational complexity is an order of magnitude smaller than the system providing optimal quality, the proposed system yields quality results very similar to those of the optimal system.

Reducing the file size of a JPEG image to meet bandwidth or terminal constraints is a common transcoding operation. The reduction can be achieved by reducing either the quality factor (QF) or the resolution, or both. In this paper, we analyze the impact of QF and scaling parameter choices on the quality of the resulting images, as measured by a quality metric such as the structural similarity index (SSIM). We propose a quality-aware transcoding system which considers the quality of transcoded images when QF and scaling are selected jointly. Its goal is to select QF and scaling parameters that maximize the user experience under a given viewing condition, as measured by the chosen quality metric.

This work presents two new algorithms to predict the file size of a JPEG image subject to transformations consisting of simultaneous changes in resolution (scaling) and in quality factor (QF). To be computationally efficient, the prediction is based solely on easily accessible image parameters such as the quality factor and the original file size. A large image corpus (100,000 images), gathered by a crawler, is divided into a training set used to optimize the predictors and into a test set used to validate the predictors. For both algorithms the prediction error is shown to be of a few percents when the output parameters are close to those of the original image while remaining reasonably attractive elsewhere. Both algorithms are simple to implement and require very little processing for the prediction itself; making them good choices for implementation in transcoding servers. Following is an example of a prediction matrix from the first algorithm for images with original quality factor (QFin) of 80 and for various scalings and output quality factors (QFout).

Very sparse bitmaps are used in a wide variety of applications, ranging from adjacency matrices in representation of large sparse graphs, representation of sparse space occupancy to book-keeping in databases. A method based on pruning of the binary space partition (BSP) tree in the minimal description length (MDL) principle for coding very sparse bitmaps was proposed. This new method for coding of sparse bitmaps meets seemingly competing objectives of good compression, the ability of conducting queries directly in the compression domain, and simple and fast decoding.

This paper shows the existence of binary pseudowavelets, bases on the binary domain that exhibit some of the properties of wavelets, such as multiresolution reconstruction and compact support. The binary pseudowavelets are defined on B/sup n/ (binary vectors of length n) and are operated upon with the binary operators logical and, and exclusive or. The forward transform, or analysis, is the decomposition of a binary vector into its constituent binary pseudowavelets. Binary pseudowavelets allow multiresolution, progressive reconstruction of binary vectors by using progressively more coefficients in the inverse transform. Binary pseudowavelets bases, being sparse matrices, also provide for fast transforms; moreover pseudowavelets rely on hardware-friendly operations for efficient software and hardware implementation.

Summary form only given. This article describes a component of the DejaVu color document image compression. The DejaVu scheme consists in separating the image into (a) a foreground layer containing text and drawings, and (b) a background layer containing background color and the pictures. These two components are then compressed using the most appropriate techniques. This article outlines a wavelet method for encoding the background layer without wasting bits on pixels masked by the foreground layer. Our method handles arbitrarily complex masks with reasonable computational requirements.

Summary form only given. The problem of computing the minimum redundancy codes as we observe symbols one by one has received a lot of attention. However, existing algorithms implicitly assumes that either we have a small alphabet or that we have an arbitrary amount of memory at our disposal for the creation of a coding tree. In real life applications one may need to encode symbols coming from a much larger alphabet, for e.g. coding integers. We introduce a new algorithm for adaptive Huffman coding, called algorithm M, that uses space proportional to the number of frequency classes. The algorithm uses a tree with leaves that represent sets of symbols with the same frequency, rather than individual symbols. The code for each symbol is therefore composed of a prefix (specifying the set, or the leaf of the tree) and a suffix (specifying the symbol within the set of same-frequency symbols). The algorithm uses only two operations to remain as close as possible to the optimal: set migration and rebalancing. We analyze the computational complexity of algorithm M, and point to its advantages in terms of low memory complexity and fast decoding. Comparative experiments were performed with algorithm M on the Calgary corpus, with static Huffman coding as well as with another adaptive Huffman coding algorithms, algorithm /spl Lambda/ of Vitter. Experiments show that M performs comparably or better than the other algorithms but requires much less memory. Finally, we present an improved algorithm, M/sup +/, for non-stationary data, which models the distribution of the data in a fixed-size window in the data sequence.