VLSI Design of a Fast Pipelined 8x8 Discrete Cosine Transform

Zabidi, Nurulnajah Mohd; Rahman, Ab Al-Hadi Ab

doi:10.11591/ijece.v7i3.pp1430-1435

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…They are both accurate, but CNN is more preferred as it is proven to be robust to brightness, image size and tongue position after it achieved generalization. Data augmentation methods can be adaptively performed to increase the number of samples and improve recognition accuracy [23,24,25]. CNN is said to be more sensitive to brightness condition.…”

Section: Related Workmentioning

confidence: 99%

Tongue Colour Diagnosis System Using Convolutional Neural Network

Tan

Rusli

Kamarudin

et al. 2022

J. Phys.: Conf. Ser.

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Tongue diagnosis is known as one of the effective and yet noninvasive techniques to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine and traditional Korean medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape and texture. Thus, research of automatic tongue diagnosis system is needed for aiding practitioners in recognizing the features for tongue diagnosis. In this paper, a tongue diagnosis system based on Convolution Neural Network (CNN) for classifying tongue colours is proposed. The system extracts all relevant information (i.e., features) from three-dimensional digital tongue image and classifies the image into one of the colours (i.e. red or pink). Several pre-processing and data augmentation methods have been carried out and evaluated, which include salt-and-pepper noises, rotations and flips. The proposed system achieves accuracy of up to 88.98% from 5-fold cross validation. Compared to the reported baseline Support Vector Machine (SVM) method, the proposed method using CNN results in roughly 30% improvement in recognition accuracy.

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Section: Related Workmentioning

confidence: 99%

Tongue Colour Diagnosis System Using Convolutional Neural Network

Tan

Rusli

Kamarudin

et al. 2022

J. Phys.: Conf. Ser.

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“…The elements of the forward transform matrix are denoted in the HEVC standard [2,20]. The smaller size of transform matrices can be derived from the 32x32 matrix as shown in (4).…”

Section: Dct Theorymentioning

confidence: 99%

“…HEVC achieves double rate better video compression efficiency compared to AVC/H.264 standard [1,3]. However, the TU sizes for DCT in AVC/H.264 is only 8x8, as implemented in [4][5][6][7]. The function of DCT is to reduce the redundancies by transforming the spatial domain into the spectral domain and it is widely used in image and video compression technology.…”

Section: Introductionmentioning

confidence: 99%

HEVC 2D-DCT architectures comparison for FPGA and ASIC implementations

et al. 2019

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This paper compares ASIC and FPGA implementations of two commonly used architectures for 2-dimensional discrete cosine transform (DCT), the parallel and folded architectures. The DCT has been designed for sizes 4x4, 8x8, and 16x16, and implemented on Silterra 180nm ASIC and Xilinx Kintex Ultrascale FPGA. The objective is to determine suitable low energy architectures to be used as their characteristics greatly differ in terms of cells usage, placement and routing methods on these platforms. The parallel and folded DCT architectures for all three sizes have been designed using Verilog HDL, including the basic serializer-deserializer input and output. Results show that for large size transform of 16x16, ASIC parallel architecture results in roughly 30% less energy compared to folded architecture. As for FPGAs, folded architecture results in roughly 34% less energy compared to parallel architecture. In terms of overall energy consumption between 180nm ASIC and Xilinx Ultrascale, ASIC implementation results in about 58% less energy compared to the FPGA.

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“…Because of those abovementioned properties, it necessary to include the pipeline and parallelism stage on whole intra prediction design. The advantage of the pipeline is more detailed in work [19], which the authors are used a fast VLSI pipelined multiplerless fixed-point 8x8 DCT. These architectures can be improved when applied 2-stage pipeline than non-pipeline.…”

Section: Pementioning

confidence: 99%

An energy-aware system-on-chip architecture for intra prediction in HEVC standard

Ansari¹,

Mansouri²,

Ahaitouf³

2019

IJECE

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<p>High resolution 4K and 8K are becoming the more used in video applications. Those resolutions are well supported in the new HEVC standard. Thus, embedded solutions such as development of dedicated ystems-On-Chips (SOC) to accelerate video processing on one chip instead of only software solutions are commendable. This paper proposes a novel parallel and high efficient hardware accelerator for the intra prediction block. This accelerator achieves a high-speed treatment due to pipelined processing units and parallel shaped architecture. The complexity of memory access is also reduced thanks to the proposed design with less increased power consumption. The implementation was performed on the 7 Series FPGA 28 nm technology resources on Zynq-7000 and results show, that the proposed architecture takes 16520 LUTs and can reach 143.65 MHz as a maximum frequency and it is able to support the throughput of 3840×2160 sequence at 90 frames per second.</p>

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VLSI Design of a Fast Pipelined 8x8 Discrete Cosine Transform

Cited by 9 publications

References 8 publications

Tongue Colour Diagnosis System Using Convolutional Neural Network

Tongue Colour Diagnosis System Using Convolutional Neural Network

HEVC 2D-DCT architectures comparison for FPGA and ASIC implementations

An energy-aware system-on-chip architecture for intra prediction in HEVC standard

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