Marlin : a high throughput variable-to-fixed codec using plurally parsable dictionaries

Altres ajuts: this work is also partially supported by the German Federal Ministry of Education and Research (BMBF) within the SPHERE project.We present Marlin, a variable-to-fixed (VF) codec optimized for decoding speed. Marlin builds upon a novel way of constructing VF dictionaries that maximizes efficiency for a given dictionary size. On a lossless image coding experiment, Marlin achieves a compression ratio of 1.94 at 2494MiB/s. Marlin is as fast as state-of-the-art high-throughput codecs (e.g., Snappy, 1.24 at 2643MiB/s), and its compression ratio is close to the best entropy codecs (e.g., FiniteStateEntropy, 2.06 at 523MiB/s). Therefore, Marlin enables efficient and high- throughput encoding for memoryless sources, which was not possible until now

Compressive Sampling of Speech Signals

Compressive sampling is an evolving technique that promises to effectively recover a sparsesignal from far fewer measurements than its dimension. The compressive sampling theoryassures almost an exact recovery of a sparse signal if the signal is sensed randomly where thenumber of the measurements taken is proportional to the sparsity level and a log factor of thesignal dimension. Encouraged by this emerging technique, we study the application ofcompressive sampling to speech signals.The speech signal is very dense in its natural domain; however speech residuals obtainedfrom linear prediction analysis of speech are nearly sparse. We apply compressive sampling tospeech signals, not directly but on the speech residuals obtained by conventional and robustlinear prediction techniques. We use a random measurement matrix to acquire the data then use§¤-1 minimization algorithms to recover the data. The recovered residuals are then used tosynthesize the speech signal. It was found that the compressive sampling process successfullyrecovers speech recorded both in clean and noisy environments. We further show that the qualityof the speech resulting from the compressed sampling process can be considerably enhanced byspectrally shaping the error spectrum. The recovered speech quality is said to be of high qualitywith SNR up to 15 dB at a compression factor of 0.4

LAPLACIAN MODELING OF DCT COEFFICIENTS FOR REAL-TIME ENCODING

Digital image/video coding standards such as JPEG, H.264 are becoming more and more important for multimedia applications. Due to the huge amount of computations, there are significant efforts to speed up the encoding process. This paper proposes a Laplacian based statistical model to predict zero-quantized DCT coefficients in JPEG and to reduce the computations of encoding process. Compared with the standard JPEG and the reference in the literature, the proposed model can significantly simplify the computational complexity and achieve the best real-time performance at the expense of negligible visual degradation. Moreover, it can be directly applied to other DCT-based image/video codec. Computational reduction also implies longer battery lifetime and energy economy for digital applications

Laplacian modeling of DCT coefficients for real-time encoding

Digital image/video coding standards such as JPEG, H.264 are becoming more and more important for multimedia applications. Due to the huge amount of computations, there are significant efforts to speed up the encoding process. This paper proposes a Laplacian based statistical model to predict zero-quantized DCT coefficients in JPEG and to reduce the computations of encoding process. Compared with the standard JPEG and the reference in the literature, the proposed model can significantly simplify the computational complexity and achieve the best real-time performance at the expense of negligible visual degradation. Moreover, it can be directly applied to other DCT-based image/video codec. Computational reduction also implies longer battery lifetime and energy economy for digital applications. Index Terms — Discrete cosine transform (DCT), quantization, computational complexity, real-time encoding 1