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We present a novel method for the design of finite impulse response (FIR) filters with discrete coefficients that belong in the sum of powers-of-two (POT) space. The importance of this class of filters cannot be overstated, given the ever-increasing number of applications for which a specific hardware implementation is needed. Filters that have coefficients that belong to such a class are also referred to as multiplierless filters, given that the operations performed by the filter can all be implemented by using appropriately designed shifts of the input data, making them a perfect choice whenever implementation simplicity and processing speed are the ultimate goal. To produce such a design, we employ a vector successive approximation technique successfully used in data compression that has a very low computational complexity, the Matching Pursuits Generalized BitPlanes algorithm (MPGBP). We derive optimality conditions for the approximation dictionary. We compare filters obtained with the proposed method with those derived in previous works. Based on this comparative analysis, we show that this new and powerful way of producing the filters' coefficients is also among the simplest available in the literature. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a pattern-matching-based algorithm for video compression. This algorithm, named multidimensional multiscale parser (MMP)-Video, is based on the H.264/AVC video encoder, but uses a pattern-matching paradigm instead of the state-of-the-art transform-quantization-entropy encoding approach. The proposed method adopts the use of multiscale recurrent patterns to compress both spatial and temporal prediction residues, totally replacing the use of transforms and quantization. Experimental results show that the coding performance of MMP-Video is better than the one of H.264/AVC high profile, especially for medium to high bit-rates. The gains range up to 0.7 dB, showing that, in spite of its larger computational complexity, the use of multiscale recurrent pattern matching paradigm deserves being investigated as an alternative for video compression. [ABSTRACT FROM AUTHOR]

In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves rele- vant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types. [ABSTRACT FROM AUTHOR]