Image Compression using Orthogonal Wavelets Viewed from Peak Signal to Noise Ratio and Computation Time

Prasad, P. M. K.; Prabhakar, Telagarapu; Madhuri, G.

doi:10.5120/7177-9828

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…The objective evaluation is based on the difference between the pixels and structures of the two images, and the evaluation result of the image translation quality is calculated by the formula. Commonly used objective evaluation indicators are mean square error [11] , peak signal to noise ratio [12] , structural similarity [13][14][15] , L1 loss and cosine similarity [16] .…”

Section: Image Quality Assessment Methodsmentioning

confidence: 99%

Research on Image Translation and Image Quality Evaluation Based on Generative Adversarial Networks

Chang¹,

Yude²,

Ji³

2020

dtcse

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How to correctly evaluate the quality of image translation is an important research topic. In this paper, the effects of the hyperparameters and algorithm optimization methods of Pix2Pix model on image translation quality are studied experimentally, and the model parameters and algorithm optimization methods are determined. The average subjective score, peak signal-to-noise ratio and structural similarity of image translation effects are proposed. And other subjective and objective indicators. On the CUFS face database, the image is translated based on the Pix2Pix model and the image translation results are evaluated. Analyze the results of image translation evaluation. The optimization method of image translation algorithm chooses Adam. When the learning rate is 0.001, the subjective index of image translation averages above 4 points, PSNR reaches 14.35, SSIM reaches 0.58, L1 loss reaches 29975251 and above, and cosin is above 0.97, are better than other methods. Finally, the validity of the image quality assessment indicators in this paper is verified on the conditional generation confrontation network model.

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Section: Image Quality Assessment Methodsmentioning

confidence: 99%

Research on Image Translation and Image Quality Evaluation Based on Generative Adversarial Networks

Chang¹,

Yude²,

Ji³

2020

dtcse

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“…It is based on error-sensitive image quality evaluation. The smaller the PSNR value, the better the image quality [49,50]. PSNR can be calculated by the following formula:…”

Section: Image Synthesis Quality Assessmentmentioning

confidence: 99%

M-StruGAN: An Automatic 2D-Plan Generation System under Mixed Structural Constraints for Homestays

Gao

Guo

2023

Sustainability

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Existing methods for generating 2D plans based on intelligent systems usually require human-defined rules, and their operations are complex. GANs can solve these problems through independent research and learning. However, they only have generative design research based on a single constraint condition, and whether they can generate a qualified design scheme under many constraints is still unclear. Therefore, this paper develops the M-StruGAN generative model based on the structural design framework of a GAN. Its application research is extended to the 2D-plan layout generation of homestay based on the constraints of hybrid structures, and the feasibility of the method is comprehensively verified through three aspects: image synthesis quality assessment, scheme rationality assessment, and scheme design quality assessment. Experimental results show that the quality of the drawings generated by M-StruGAN is qualified, designers have a high degree of acceptance of the design results of M-StruGAN, and M-StruGAN completed the learning of the critical points of the 2D layout. Finally, through the human–computer interaction application of M-StruGAN, it can be found that compared with traditional design methods, M-StruGAN based on pix2pixHD has high-definition image quality, higher design efficiency, lower design cost, and more stable design quality.

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“…Wavelet analysis can be used to divide the information of an image into approximation and detail sub signals. The approximation sub signal shows the general trend of pixel values, and three detail sub signals show the vertical, horizontal and diagonal details or fast changes in the image [5]. In the decomposition level one, the image will be divided into 4 sub-bands, called LL, LH, HL, and HH.…”

Section: Compression Using Wavelet Transformmentioning

confidence: 99%

“…A good quality compression is generally achieved in the process of memory consolidation, which generates a small reduction, and vice versa. The quality of an image is subjective and relative, depending on the observation of the user [5]. Compression ratio is the ratio of number of bits required to represent the data before compression to the number of bits required to represent data after compression.…”

Section: Compression Using Wavelet Transformmentioning

confidence: 99%

Mixed Transforms Generated by Tensor Product and Applied in Data Processing

Mohammed¹,

Ibraheem²,

Jassim³

2013

IJCA

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Finding orthogonal matrices in different sizes is very complex and important because it can be used in different applications like image processing and communications (e.g. CDMA and OFDM). In this paper we introduce a new method to find orthogonal matrices by using tensor products between two or more orthogonal matrices of real and imaginary numbers with applying it in images and communication signals processing. The output matrices will be orthogonal matrices too and the processing by our new method is very easy compared to other classical methods those use basic proofs .The results are normal and acceptable in communication signals and images but it needs more research works.

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Image Compression using Orthogonal Wavelets Viewed from Peak Signal to Noise Ratio and Computation Time

Cited by 4 publications

References 5 publications

Research on Image Translation and Image Quality Evaluation Based on Generative Adversarial Networks

Research on Image Translation and Image Quality Evaluation Based on Generative Adversarial Networks

M-StruGAN: An Automatic 2D-Plan Generation System under Mixed Structural Constraints for Homestays

Mixed Transforms Generated by Tensor Product and Applied in Data Processing

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