Abstract
This paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). In order to reduce the memory requirement of the proposed architecture, only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFr WF m , we develop a multiplier-less LFrWF version, i.e., the LFr WF ml , which reduces the critical path delay (CPD) to the delay T a of an adder. The proposed LFr WF m and LFr WF ml architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. Moreover, the proposed LFr WF m and LFr WF ml architectures, along with the state-of-the-art FrWF architectures (with multipliers (Fr WF m ) and without multipliers (Fr WF ml )) are compared through implementation on the same FPGA board. The LFr WF m requires 22% less look-up tables (LUT), 34% less flip-flops (FF), and 50% less compute cycles (CC) and consumes 65% less energy than the Fr WF m . Also, the proposed LFr WF ml architecture requires 50% less CC and consumes 43% less energy than the Fr WF ml . Thus, the proposed LFr WF m and LFr WF ml architectures appear suitable for computing the DWT of images on wearable sensors.
Highlights
An interconnection of visual sensor nodes is known as visual sensor network (VSN) [13, 14] or as wireless multimedia sensor network (WMSN) [15, 16]
In many visual applications of wearable sensors and portable imaging devices, images captured by the camera need to be transmitted wirelessly to a body-worn or nearby hub device. e wearable sensors and portable imaging devices have limited resources, and the wireless links have narrow bandwidth [28], making it impossible to directly send the raw images. us, there is a need to compress the images before transmission [29]. erefore, an image coder is needed in order to compress the images
An image is generally first transformed using the discrete cosine transform (DCT) [30] or discrete wavelet transform (DWT) [31, 32] and it is quantized and entropy coded. e DWT, which is used in JPEG 2000
Summary
Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors. Is paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). Aside from an LFrWF version with multipliers, i.e., the LFrWFm, we develop a multiplier-less LFrWF version, i.e., the LFrWFml, which reduces the critical path delay (CPD) to the delay Ta of an adder. E proposed LFrWFm and LFrWFml architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. The proposed LFrWFm and LFrWFml architectures, along with the state-of-the-art FrWF architectures (with multipliers (FrWFm) and without multipliers (FrWFml)) are compared through implementation on the same FPGA board. The proposed LFrWFml architecture requires 50% less CC and consumes 43% less energy than the FrWFml. us, the proposed LFrWFm and LFrWFml architectures appear suitable for computing the DWT of images on wearable sensors
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