In today’s communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2:15% and 14:10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively.

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An Improved JPEG Image Compression Technique based on Selective Quantization

Scale invariant feature transform (SIFT) points are scale-space extreme points, representing local minutiae features in the Gaussian scale space. SIFT intensity ratio (SIR), as a novel reduced-reference metric, is feasible to assess various common distortions without the prior knowledge of distortion types. It describes relative changes in the number of SIFT points between a test image and its corresponding reference image. SIFT points in the metric are detected in the first octave of the difference-of-Gaussian scale space under certain preprocessings: neighborhood enhancement through a Laplacian operator to sharpen isolated points and thin edges, reducing false SIFT points; double-size image magnification through linear interpolation to amplify distortion effects, improving its sensitivity to image distortions. Experimental results demonstrate that SIR is superior to existing classic reduced-reference metrics, and can be used to assess different distortions.

Reduced-reference image quality assessment through SIFT intensity ratio

With the increase of distance and the influence of environmental factors, such as illumination and haze, the face recognition accuracy is significantly lower than that of indoor close-up images. In order to solve this problem, an effective face image enhancement method is proposed in this paper. This algorithm is a nonlinear transformation which combines gamma and logarithm transformation. Therefore, it is called: G-log. The G-Log algorithm can perform the following functions: (1) eliminate the influence of illumination; (2) increase image contrast and equalize histogram; (3) restore the high-frequency components and detailed information; (4) improve visual effect; (5) enhance recognition accuracy. Given a probe image, the procedure of face alignment, enhancement and matching is executed against all gallery images. For comparing the effects of different enhancement algorithms, all probe images are processed by different enhancement methods and identical face alignment, recognition modules. Experiment results show that G-Log method achieves the best effect both in matching accuracy and visual effect. Long-distance uncontrolled environment face recognition accuracy has been greatly improved, up to 98%, 98%, 95% for 60-, 100-, 150-m images after processed by G-Log from original 95%, 89%, 70%.

Long-Distance/Environment Face Image Enhancement Method for Recognition

The JPEG Images play a significant role in present multimedia based computing industry. Being a popular lossy mode of image compression, The JPEG has extensively been being used in almost all sorts of digital device including the mobile phones, tablet and handheld computers. Although the popularly used Baseline JPEG Algorithm is an easy one to be performed by the powerful processors, sti ll the small devices of less capable processors suffer a lot from encoding or decoding a JPEG image by the Baseline JPEG Algorithm. This is due to some complex computations required by Baseline JPEG. This paper discovers the computational cost currently needed by Baseline JPEG and suggests an efficient way to encode or decode the JPEG images so that the overall computational cost of the Baseline JPEG Algorithm is reduced with less affecting the obtainable Compression Ratio and Peak Signal to Noise Ratio (PSNR). The suggested cost reduction technique has been tested upon some small computing devices and comparative cost analysis is presented.

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Computational complexity reduction of jpeg images

Digital Forensics is the branch of science dealing with investigation of evidences recovered from digital devices, to safeguard against rapidly increasing cyber crimes in today’s digital world. The Source Camera Identification (SCI) problem is to map an image under question correctly to its source device. Following a Digital Forensic approach, the source of an image is detected by post–priori investigation of traces left behind in the image, by the camera. Such traces are generated due to the post–processing operations an image undergoes inside a digital camera, after being captured. In this paper, we model the SCI problem as a machine learning classification problem and focus on the most crucial component of a learning model, i.e. feature selection. We propose three different techniques for feature selection: Filter based approach, Wrapper based approach using Genetic Algorithm (GA), and also a hybrid approach with both Filter and Wrapper methods combined together. We investigate the source detection accuracy that each technique succeeds to achieve. Our experimental results suggest that the proposed methods produced a much compact feature set, hence considerably improve the source detection accuracy and minimize the training time of the learning model, as compared to the state–of–the–art.

Digital Forensic Source Camera Identification with Efficient Feature Selection Using Filter, Wrapper and Hybrid Approaches

We propose a new method for blockiness distortions identification and quality assessment of JPEG images in the absence of any reference images using Haar wavelet decomposition. Blockiness artifacts contaminating JPEG encoded images can be modeled as a particular edge structure that transforms into another when subjected to edge detection algorithm. Some features that give meaningful information for blockiness distortions and quality assessment are subsequently derived from edge detected image by means of 3-level Haar wavelet transform. The validity of our quality assessment technique was tested against publicly available images quality assessment database whilst the proposed blockiness detector was evaluated subjectively using our own subjective test. Our experiments show that the proposed quality metric and blockiness distortions detector demonstrate satisfying performances.

http://ieeexplore.ieee.org/iel5/6132213/6140728/06140748.pdf

Haar wavelet decomposition based blockiness detector and picture quality assessment method for JPEG images

This paper presents a novel method of no-reference image quality assessment for JPEG encoded images by means of multiresolution analysis using Haar wavelet decomposition. The proposed method takes advantage of the fact that JPEG encoded images are usually contaminated with blockiness artifacts. Blockiness artifact is modeled as a particular edge structure that transforms into a different edge structure when edge detection algorithm is applied. Subsequently after edge detection is performed, a 3-level Haar Wavelet Transform (HWT) is employed to construct an edge map, from which some features are derived. These features give meaningful information for blockiness distortions identification and quality assessment. The proposed quality metric was tested against publicly available JPEG subset of LIVE Image Database, whilst the detection algorithm was evaluated subjectively in terms of how well the automatic detection agrees with human’s perceived view. The detection algorithms as well as the proposed JPEG quality metric demonstrate satisfying performances.

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No-Reference JPEG Image Quality Assessment Using Haar Wavelet Decomposition

We propose a new Reduced Reference model for assessing the quality of JPEG and JPEG2000 images We utilize the Haar Wavelet Decomposition as a tool to model the image information along with the distortions presents in JPEG or JPEG2000 images One of the superiority of our method is the ability to independently assess the quality of JPEG and JPEG2000 images without using any additional discrimination method Our method also incorporate a simple design to model the distortions in the image without using complex Human Visual System (HVS) model Despite its simple design, our method has demonstrated an accurate and reliable quality metric Experimental results using several categories of JPEG and JPEG2000 images using LIVE Image Database Release 2 will be presented to verify our metric The results of our experiments show that the method has achieved high correlation with the subjective data in most of the categories

Haar wavelet based reduced reference quality assessment technique for JPEG/JPEG2000 images

The development of information technology allows many people to communicate at any time with various media, one of which is the exchange of messages. However, many people do not realize that there are security holes that are used by irresponsible parties to commit crimes such as theft of messages, intercepting messages, and changing the contents of messages. One technique for securing messages is cryptography. Cryptography is the science and art of keeping messages secure when messages are sent from one place to another. Several cryptographic methods that can be used are Advanced Encryption Standard and Caesar Cipher, because the Advanced Encryption Standard method has high security for message security while the Caesar Cipher method has the advantage of being fast in calculations. The combination of these two algorithms in making secret messages is implemented in the form of a mobile-based application to make it easier for users to make secret messages. The results of the encrypted messages formed are difficult to decrypt because they go through 2 stages of the encryption process so that the contents of important messages can be guaranteed confidentiality.

https://www.publikasiilmiah.unwahas.ac.id/index.php/JINRPL/article/download/6714/pdf

Antony Halim

Papers

Haar wavelet decomposition based blockiness detector and picture quality assessment method for JPEG images

No-Reference JPEG Image Quality Assessment Using Haar Wavelet Decomposition

Haar wavelet based reduced reference quality assessment technique for JPEG/JPEG2000 images

Implementasi Algoritma Advance Encryption Standard dan Caesar Cipher pada Pesan Terenkripsi