A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions

Bajit, Abderrahim; Nahid, Mohammed; Tamtaoui, Ahmed; Benbrahim, Mohammed

doi:10.25046/aj050229

Cited by 14 publications

(5 citation statements)

References 34 publications

(106 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the transfer of images between communication paths is a costly process, the main goal in image compression is to reduce the number of bits needed to display an image [3]. As we know, the identification of data redundancy is the most important part in image compression, and among the types of data redundancy, we can mention cryptographic redundancy [4], inter-pixel redundancy [5] and psychooptical redundancy [6]. The main goal of image compression algorithms is to try to reduce all types of image redundancies, but which type of algorithm to use depends on how to use the information in the image.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

View full text Add to dashboard Cite

One of the important research areas in imaging is the formation of images, which plays an important role in many different applications, including surveillance, control, and security affairs. On the other hand, high spatial resolution is one of the most important factors for increasing image quality, but it increases the amount of storage memory. Based on this, it is expected that the processing methods will be concentrated in this area. In face recognition systems, one of the existing challenges is maintaining the image recognition rate. These images in the recognition system may affect the efficiency of the system, and as a result, it may be inferred that the percentage of recognition is found. Proposing a method that at least does not reduce detection rates would be very desirable. This article investigates how to compress facial images with high spatial resolution using innovative algorithms to reduce or even increase their accuracy as much as possible. In this article, meta-heuristic algorithms (genetics and gray wolf) are used in a way that they are responsible for identifying the important and similar areas of matching macroblocks in the whole image segmentation. So how arranging the length of the bit string of each block is appropriate to achieve the target estimation to achieve the highest recognition accuracy value, PSNR value, and SSIM value of the image set? In the simulation and evaluation section, the facial images of the CIE and FEI databases have been examined as a selective study, and the recognition efficiency of the images for the conditions without/with compression and the common SPIHT and JPEG coding methods and compared with the proposed method. The simulation results show the significant impact of the proposed methods using meta-heuristic algorithms in increasing the quality of PSNR and SSIM in contrast to the recognition efficiency. According to the proposed method, the larger the value of dividing the blocks, the better the average PSNR and SSIM, assuming the highest recognition accuracy can be reached in both meta-heuristic algorithms. In general, depending on the type of application of the problem, there is a compromise to achieve the highest average PSNR or SSIM, using a genetic algorithm or gray wolf. The gray wolf algorithm, however, reaches its optimal answer much faster than the genetic algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

khodadadi

Ardeshir

Grailu

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…When comparing the proposed method with the SPIHT coding curve and evaluating its recognition efficiency in accordance with the bit rate criterion, it is apparent that it can maintain significant and effective features in recognition. In Table (6), we present the percentage of recognition rate and the performance efficiency of the proposed method compared with the without compression method (Original) and the compression methods (SPIHT and JPEG).…”

Section: Test Resultsmentioning

confidence: 99%

“…The primary objective of image compression is to reduce the number of bits necessary to display an image, since the transfer of images is a costly process [3]. Image compression begins with identifying data redundancy, and among the types of data redundancy, we can mention cryptographic redundancy [4], inter-pixel redundancy [5], and psychovisual redundancy [6]. The main objective of image compression algorithms is to reduce all types of image redundancy.…”

Section: Introductionmentioning

confidence: 99%

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

khodadadi

Ardeshir

Grailu

2023

IJE

View full text Add to dashboard Cite

In image processing, compression plays an important role in monitoring, controlling, and securing the process. The spatial resolution is one of the most effective factors in improving the quality of an image; but, it increases the amount of storage memory required. Based on meta-heuristic algorithms, this article presents a compression model for face images with block division and variable bit allocation. Wavelet transform is used to reduce the dimensions of high spatial resolution face images. In order to identify important and similar areas of identical macroblocks, genetic algorithms and gray wolves are used. A bit rate allocation is calculated for each block to achieve the best recognition accuracy, average PSNR, and SSIM. The CIE and FEI databases have been used as case studies. The proposed method has been tested and compared with the accuracy of image recognition under uncompressed conditions and using the common SPIHT and JPEG coding methods. Recognition accuracy increased from 0.18% for 16×16 blocks to 1.97% for 32×32 blocks. Additionally, the gray wolf algorithm is much faster than the genetic algorithm in reaching the optimal answer. Depending on the application type of the problem, the genetic algorithm or the gray wolf may be preferred to achieve the maximum average PSNR or SSIM. At the bit rate of 0.9, the maximum average PSNR for the gray wolf algorithm is 34.92 and the maximum average SSIM for the genetic algorithm is 0.936. Simulation results indicate that the mentioned algorithms increase PSNR and SSIM by stabilizing or increasing recognition accuracy.

show abstract

“…The larger the factor determined, the higher the image quality, hence the important role this metric adopts. Similarly for foveal coding, we foveally evaluate the image quality using an FDVP metric improved from the VDP metric by applying a foveal weighting model that uses the foveal filter for the detection of areas of interest [24].…”

Section: Watermarking For Foveal and Visual Image Coding To Evaluate ...mentioning

confidence: 99%

Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

Allali¹,

Morino²,

Oussous³

et al. 2023

Information Security and Privacy in the Digital World - Some Selected Topics

Self Cite

View full text Add to dashboard Cite

To ensure copyright protection and authenticate ownership of media or entities, image watermarking techniques are utilized. This technique entails embedding hidden information about an owner in a specific entity to discover any potential ownership issues. In recent years, several authors have proposed various ways to watermarking. In computational intelligence contexts, however, there are not enough research and comparisons of watermarking approaches. Soft computing techniques are now being applied to help watermarking algorithms perform better. This chapter investigates soft computing-based image watermarking for a medical IoT platform that aims to combat the spread of COVID-19, by allowing a large number of people to simultaneously and securely access their private data, such as photos and QR codes in public places such as stadiums, supermarkets, and events with a large number of participants. Therefore, our platform is composed of QR Code, and RFID identification readers to ensure the validity of a health pass as well as an intelligent facial recognition system to verify the pass’s owner. The proposed system uses artificial intelligence, psychovisual coding, CoAP protocol, and security tools such as digital watermarking and ECC encryption to optimize the sending of data captured from citizens wishing to access a given space in terms of execution time, bandwidth, storage space, energy, and memory consumption.

show abstract

A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions

Cited by 14 publications

References 34 publications

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

WITHDRAWN: A new method of facial image compression based on meta-heuristic algorithms with variable bit budget allocation

Compressing Face Images Using Genetic and Gray Wolf Meta-heuristic Algorithms Based on Variable Bit Allocation

Application of Computational Intelligence in Visual Quality Optimization Watermarking and Coding Tools to Improve the Medical IoT Platforms Using ECC Cybersecurity Based CoAP Protocol

Contact Info

Product

Resources

About