Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful pairs. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must he defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state-of-the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated.

Developments in digital image coding tend to involve more and more complex algorithms, and require therefore an increasing amount of computation. To improve the overall system performance, some schemes apply different coding algorithms to separate parts of an image according to the content of this subimage. Such schemes are referred to as dynamic coding schemes. Applying the best suited coding algorithm to a part of an image will lead to an improved coding quality, but implies an algorithm selection phase. Current selection methods require the computation of the reconstructed image after coding and decoding with all the selected algorithms in order to choose the best method. Some other schemes use ways of pruning the search in the algorithm space. Both approaches suffer from a heavy computational load. Furthermore, the computational complexity is increased even more if the parameters have to be adjusted for a given algorithm during the search. This paper describes a way to predict the coding quality of a region of the input image for any given coding method. The system will then be able to select the best suited coding algorithm for each region according to the predicted quality. This prediction scheme has low complexity, and also enables the adjustment of algorithm specific parameters during the search.

In image coding, the choice of a good image coding algorithm is very dependent on the image content. Based on this fact, dynamic coding algorithms have been designed. They try to find an optimal coding scheme for each image segment. They rely on an exhaustive search of the best coding algorithm. Evaluation of all algorithms is computationally very intensive and strongly limits the number of considered algorithms for a given application. Therefore, current standards rely on a single coding algorithm. This paper investigates a way to predict the coding quality from the image content. This prediction is based on a neural network. The coding quality is computed from image region features. Those features are easy and fast to compute, and are common to the whole set of considered coding algorithms. Therefore, the choice of the best algorithm can be based on those predicted coding qualities, and does not require the computation of all coding algorithms. The system is also fast enough to be used for dynamic bitrate allocation, and a simple algorithm to do this is proposed.