In this paper, an efficient semi-systolic array architecture for separable 2-D Discrete Wavelet Transform (DWT) is introduced. The semi-systolic array is applicable to any convolution that requires an arbitrary subsam...
In this paper, an efficient semi-systolic array architecture for separable 2-D Discrete Wavelet Transform (DWT) is introduced. The semi-systolic array is applicable to any convolution that requires an arbitrary subsampling function. The semi-systolic array presents a better implementation of the convolution function of DWT. This kind of implementation offers a higher efficiency compared to regular systolic implementation when applied for 2-D DWT. The architecture has an efficiency of at least 91% which increases proportional to the number of octaves with no change in the architecture design except for minor modifications to the control logic and memory size. The propose architecture is scalable for different size of filter and different number of octave. The comm.nication routing is minimum since data transfers are limited to immediate neighboring processors. The components of the architecture are fairly regular and consist of minimum number of computational units which makes it a good candidate for vlsi implementation.
In this paper, a novel algorithm for low-power image coding and decoding is presented and the various inherent trade-offs are described and investigated in detail. The algorithm reduces the memory requirements of vect...
In this paper, a novel algorithm for low-power image coding and decoding is presented and the various inherent trade-offs are described and investigated in detail. The algorithm reduces the memory requirements of vector quantization, i.e., the size of memory required for the codebook and the number of memory accesses by using small codebooks. This significantly reduces the memory-related power consumption, which is an important part of the total power budget. To compensate for the loss of quality introduced by the small codebook size, simple transformations are applied on the codewords during coding. Thus, small codebooks are extended through computations and the main coding task becomes computation-based rather than memory-based. Each image block is encoded by a codeword index and a set of transformation parameters. The algorithm leads to power savings of a factor of 10 in coding and of a factor of 3 in decoding, at least in comparison to classical full-search vector quantization. In terms of SNR, the image quality is better than or comparable to that corresponding to full-search vector quantization, depending on the size of the codebook that is used. The main disadvantage of the proposed algorithm is the decrease of the compression ratio in comparison to vector quantization. The trade-off between image quality and power consumption is dominant in this algorithm and is mainly determined by the size of the codebook.
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