SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders

Tausif, Mohd; Khan, E.; Hasan, Mohd.; Reisslein, Martin

doi:10.1109/upcon.2017.8251116

Cited by 12 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, the lowest transform memory to date has been achieved by the line-based FrWF [20], [21]. However, even the FrWF is not suitable for transforming high-resolution (HR) images on low-cost memory-constrained platforms [54], as the available memory is less than that required for HR images. In order to further reduce the FrWF memory requirement, a Segmented FrWF (SFrWF) has been proposed [54].…”

Section: B Related Workmentioning

confidence: 99%

“…However, even the FrWF is not suitable for transforming high-resolution (HR) images on low-cost memory-constrained platforms [54], as the available memory is less than that required for HR images. In order to further reduce the FrWF memory requirement, a Segmented FrWF (SFrWF) has been proposed [54]. The SFrWF partitions an image line into different segments and then applies the FrWF to these segments.…”

Section: B Related Workmentioning

confidence: 99%

“…The SFrWF implements the FrWF on segmented image lines [54]. The number of multiplication operations in the SFrWF is the same as in the FrWF [54].…”

Section: Appendix C Sfrwf Analysismentioning

confidence: 99%

“…The SFrWF implements the FrWF on segmented image lines [54]. The number of multiplication operations in the SFrWF is the same as in the FrWF [54]. However, apart from the additions needed by the FrWF, the SFrWF needs n l N (Q − 1)(n l − 2) extra additions due to the overlap and add (OLA) method [70], [71] as analyzed next.…”

Section: Appendix C Sfrwf Analysismentioning

confidence: 99%

See 3 more Smart Citations

SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

et al. 2019

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel algorithm to compute the 2-D discrete wavelet transform (DWT) of high-resolution (HR) images on low-cost visual sensor and Internet of Things (IoT) nodes. The main advantages of the proposed segmented modified fractional wavelet filter (SMFrWF) are reduced computation (time) complexity and energy consumption compared to the state-of-the-art low-memory 2-D DWT computation methods. In particular, the conventional convolution-based DWT is very fast but requires large random access memory (RAM), as the entire image needs to be in the system memory. The fractional wavelet filter (FrWF) requires only a small RAM but has high complexity due to multiple readings of image lines. The proposed SMFrWF avoids the multiple readings of image lines, thus reducing the memory read access time and, thereby, the complexity. We evaluated the proposed SMFrWF through MATLAB simulations with 70 popular gray-scale test images of dimensions ranging from 256×256 up to 8192×8192 pixels. The results show that for images of size 2048 × 2048 pixels, the proposed SMFrWF (with four segments per line) has 16.8% and 53.6% lower time complexities than the conventional DWT and FrWF, respectively. The proposed SMFrWF has also been modeled in a hardware description language (HDL) and implemented on an Artix-7 field-programmable gate array (FPGA) platform to evaluate the hardware performance. We observed that the proposed SMFrWF has 65% lower energy consumption than the FrWF (both implemented on the same board). Thus, the proposed SMFrWF appears suitable for computing the wavelet transform coefficients of HR images on low-cost visual sensors and IoT platforms.INDEX TERMS Discrete wavelet transform (DWT), low memory, low complexity, low-cost portable devices, visual sensors.

show abstract

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

“…The SFrWF implements the FrWF on segmented image lines [54]. The number of multiplication operations in the SFrWF is the same as in the FrWF [54].…”

Section: Appendix C Sfrwf Analysismentioning

confidence: 99%

Section: Appendix C Sfrwf Analysismentioning

confidence: 99%

See 2 more Smart Citations

SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Only one row of the image is buffered into the filter at a time. The memory of FrWF is reduced further in segmented FrWF (SFrWF), proposed in [47]. In SFrWF, the input image is segmented in multiple sub‐images and only a single segment is read into the memory.…”

Section: Introductionmentioning

confidence: 99%

Low‐complexity block tree image coder for visual sensor networks

Lone

Khan

2020

IET image process

Self Cite

View full text Add to dashboard Cite

The wavelet block tree coding (WBTC) algorithm is an efficient wavelet-based image coder. In this coder, multiple spatial orientation trees are combined together to make a single block tree. It uses three ordered lists to keep track of significant/insignificant coefficients and sets while coding, which increases its memory requirement as well as computational complexity. Also, it uses memory inefficient conventional discrete wavelet transform (DWT) to compute the transformed coefficients. In this study, a Low-Complexity Block Tree Coding (LCBTC) algorithm that uses two state-tables and two very small lists, is proposed. Similar to WBTC, it also uses sorting and refinement passes in each bit-plane. However, it encodes the coefficients in block-tree manner using depth-first search approach to reduce the computational complexity. It uses DWT coefficients obtained from modified fractional wavelet filter (MFrWF) rather than conventional DWT, which further reduces the overall memory and complexity of the image coder. The simulation results show that the memory requirement and computational complexity of LCBTC is much less than WBTC and other state-of-the-art coding algorithms. These features make the image coder a better candidate for compression in memory constrained and real-time visual sensor networks.

show abstract

Memory-efficient architecture for FrWF-based DWT of high-resolution images for IoMT applications

Tausif

Jain

Khan

et al. 2021

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

This paper proposes a simple low memory architecture for computing discrete wavelet transform (DWT) of high-resolution (HR) images on lowcost memory-constrained sensor nodes used in visual sensor networks (VSN) or Internet of Multimedia Things (IoMT). The main feature of the proposed architecture is the novel data scanning technique that makes memory requirement independent of the image size. The proposed architecture needs only (30S) words of memory, where S is the number of parallel processing units and a critical path delay (CPD) equal to the delay of a multiplier (T m ). Furthermore, a multiplierless version of this architecture is also proposed which reduces the CPD to T a

show abstract

SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders

Cited by 12 publications

References 17 publications

SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

SMFrWF: Segmented Modified Fractional Wavelet Filter: Fast Low-Memory Discrete Wavelet Transform (DWT)

Low‐complexity block tree image coder for visual sensor networks

Memory-efficient architecture for FrWF-based DWT of high-resolution images for IoMT applications

Contact Info

Product

Resources

About