44,296 research outputs found
Peak Transform for Efficient Image Representation and Coding
Digital Object Identifier 10.1109/TIP.2007.896599In this work, we introduce a nonlinear geometric transform, called peak transform (PT), for efficient image representation
and coding. The proposed PT is able to convert
high-frequency signals into low-frequency ones, making them much easier to be compressed. Coupled with wavelet transform
and subband decomposition, the PT is able to significantly reduce signal energy in high-frequency subbands and achieve a significant transform coding gain. This has important applications in efficient
data representation and compression. To maximize the transform coding gain, we develop a dynamic programming solution for
optimum PT design. Based on PT, we design an image encoder, called the PT encoder, for efficient image compression. Our extensive
experimental results demonstrate that, in wavelet-based subband decomposition, the signal energy in high-frequency subbands can be reduced by up to 60% if a PT is applied. The
PT image encoder outperforms state-of-the-art JPEG2000 and H.264 (INTRA) encoders by up to 2-3 dB in peak signal-to-noise ratio (PSNR), especially for images with a significant amount
of high-frequency components. Our experimental results also show that the proposed PT is able to efficiently capture and preserve high-frequency image features (e.g., edges) and yields significantly improved visual quality
Adaptation of Zerotrees Using Signed Binary Digit Representations for 3D Image Coding
Zerotrees of wavelet coefficients have shown a good adaptability for the compression of three-dimensional images. EZW, the original algorithm using zerotree, shows good performance and was successfully adapted to 3D image compression. This paper focuses on the adaptation of EZW for the compression of hyperspectral images. The subordinate pass is suppressed to remove the necessity to keep the significant pixels in memory. To compensate the loss due to this removal, signed binary digit representations are used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one
Stack-run adaptive wavelet image compression
We report on the development of an adaptive wavelet image coder based on stack-run representation of the quantized coefficients. The coder works by selecting an optimal wavelet packet basis for the given image and encoding the quantization indices for significant coefficients and zero runs between coefficients using a 4-ary arithmetic coder. Due to the fact that our coder exploits the redundancies present within individual subbands, its addressing complexity is much lower than that of the wavelet zerotree coding algorithms. Experimental results show coding gains of up to 1:4dB over the benchmark wavelet coding algorithm
Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation
To date a number of studies have shown that receptive field shapes of early
sensory neurons can be reproduced by optimizing coding efficiency of natural
stimulus ensembles. A still unresolved question is whether the efficient coding
hypothesis explains formation of neurons which explicitly represent
environmental features of different functional importance. This paper proposes
that the spatial selectivity of higher auditory neurons emerges as a direct
consequence of learning efficient codes for natural binaural sounds. Firstly,
it is demonstrated that a linear efficient coding transform - Independent
Component Analysis (ICA) trained on spectrograms of naturalistic simulated
binaural sounds extracts spatial information present in the signal. A simple
hierarchical ICA extension allowing for decoding of sound position is proposed.
Furthermore, it is shown that units revealing spatial selectivity can be
learned from a binaural recording of a natural auditory scene. In both cases a
relatively small subpopulation of learned spectrogram features suffices to
perform accurate sound localization. Representation of the auditory space is
therefore learned in a purely unsupervised way by maximizing the coding
efficiency and without any task-specific constraints. This results imply that
efficient coding is a useful strategy for learning structures which allow for
making behaviorally vital inferences about the environment.Comment: 22 pages, 9 figure
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