In this letter, we conceive a multilayered coding scheme for the recently proposed layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) system, which exploits its layered struc...
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In this letter, we conceive a multilayered coding scheme for the recently proposed layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) system, which exploits its layered structure for protecting the information bits transmitted in the base layer. Explicitly, we intrinsically integrate error correction into the layered structure of the LACO-OFDM system and invoke soft iterative decoding between the layers at the receiver. Our system operates within 2 dB of the capacity limit at a bit error ratio of 10(-4).
In this paper, we propose a cascaded sparse/DCT (S/DCT) two-layer representation of prediction residuals, and implement this idea on top of the state-of-the-art high efficiency video coding (HEVC) standard. First, a d...
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In this paper, we propose a cascaded sparse/DCT (S/DCT) two-layer representation of prediction residuals, and implement this idea on top of the state-of-the-art high efficiency video coding (HEVC) standard. First, a dictionary is adaptively trained to contain featured patterns of residual signals so that a high portion of energy in a structured residual can be efficiently coded via sparse coding. It is observed that the sparse representation alone is less effective in the R-D performance due to the side information overhead at higher bit rates. To overcome this problem, the DCT representation is cascaded at the second stage. It is applied to the remaining signal to improve coding efficiency. The two representations successfully complement each other. It is demonstrated by experimental results that the proposed algorithm outperforms the HEVC reference codec HM5.0 in the Common Test Condition.
The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coher...
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The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coherent layers: piecewise smooth regions layer, textures layer, etc. The multilayered decomposition algorithm consists in a cascade of compressions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a different transform, or basis, at a different bitrate, and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multilayer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multilayered representation has a lot of beautiful applications in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities.
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