17,418 research outputs found

    An Efficient Test Vector Compression Technique Based on Block Merging

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    In this paper, we present a new test data compression technique based on block merging. The technique capitalizes on the fact that many consecutive blocks of the test data can be merged together. Compression is achieved by storing the merged block and the number of blocks merged. It also takes advantage of cases where the merged block can be filled by all 0’s or all 1’s. Test data decompression is performed on chip using a simple circuitry that repeats the merged block the required number of times. The decompression circuitry has the advantage of being test data independent. Experimental results on benchmark circuits demonstrate the effectiveness of the proposed technique compared to previous approaches

    An Efficient Test Vector Compression Technique Based on Block Merging

    Get PDF
    In this paper, we present a new test data compression technique based on block merging. The technique capitalizes on the fact that many consecutive blocks of the test data can be merged together. Compression is achieved by storing the merged block and the number of blocks merged. It also takes advantage of cases where the merged block can be filled by all 0’s or all 1’s. Test data decompression is performed on chip using a simple circuitry that repeats the merged block the required number of times. The decompression circuitry has the advantage of being test data independent. Experimental results on benchmark circuits demonstrate the effectiveness of the proposed technique compared to previous approaches

    Tight and simple Web graph compression

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    Analysing Web graphs has applications in determining page ranks, fighting Web spam, detecting communities and mirror sites, and more. This study is however hampered by the necessity of storing a major part of huge graphs in the external memory, which prevents efficient random access to edge (hyperlink) lists. A number of algorithms involving compression techniques have thus been presented, to represent Web graphs succinctly but also providing random access. Those techniques are usually based on differential encodings of the adjacency lists, finding repeating nodes or node regions in the successive lists, more general grammar-based transformations or 2-dimensional representations of the binary matrix of the graph. In this paper we present two Web graph compression algorithms. The first can be seen as engineering of the Boldi and Vigna (2004) method. We extend the notion of similarity between link lists, and use a more compact encoding of residuals. The algorithm works on blocks of varying size (in the number of input lines) and sacrifices access time for better compression ratio, achieving more succinct graph representation than other algorithms reported in the literature. The second algorithm works on blocks of the same size, in the number of input lines, and its key mechanism is merging the block into a single ordered list. This method achieves much more attractive space-time tradeoffs.Comment: 15 page

    Multi-loop quality scalability based on high efficiency video coding

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    Scalable video coding performance largely depends on the underlying single layer coding efficiency. In this paper, the quality scalability capabilities are evaluated on a base of the new High Efficiency Video Coding (HEVC) standard under development. To enable the evaluation, a multi-loop codec has been designed using HEVC. Adaptive inter-layer prediction is realized by including the lower layer in the reference list of the enhancement layer. As a result, adaptive scalability on frame level and on prediction unit level is accomplished. Compared to single layer coding, 19.4% Bjontegaard Delta bitrate increase is measured over approximately a 30dB to 40dB PSNR range. When compared to simulcast, 20.6% bitrate reduction can be achieved. Under equivalent conditions, the presented technique achieves 43.8% bitrate reduction over Coarse Grain Scalability of the SVC - H.264/AVC-based standard
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