JPEG Compression Ratio Research Articles

Effects of Color Quantization on JPEG Compression

This paper analyzes the effects of color quantization on standard JPEG compression. Optimized color palettes were used to quantize natural images, using dithering and chroma subsampling as optional. The resulting variations on file size and quantitative quality measures were analyzed. Preliminary results, using a small image database, show that file size suffered an average 20% increase and a concomitant loss in quality was perceived ([Formula: see text]6dB PSNR, [Formula: see text]0.16 SSIM and [Formula: see text]9.6 Butteraugli). Color quantization present itself as an ineffective tool on JPEG compression but if necessarily imposed, on high quality compressed images, it might lead to a negligible increase in data size and quality loss. In addition dithering seems to always decrease JPEG compression ratio.

Predicting human complexity perception of real-world scenes.

Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction versus feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalized for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network (CNN) trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75 020 2-alternative forced choice paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r = 0.83. By contrast, feature-based models used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.

A forensic algorithm against median filtering based on coefficients of image blocks in frequency domain

Median filtering is a popular nonlinear denoising operator, it not only can be used for image enhancement, and it also is an effective tool in application of anti-forensics. So, the blind detection of median filtering is a particularly hot topic. Different from the existing median filtering forensic methods using the image pixel statistical features, this paper proposed a novel approach for detecting median filtering in digital images using coefficients of image blocks in frequency domain, based on the theory analysis and experiments test. Large numbers of experimental results show that the proposed approach achieved a high accuracy in median filtering detection and a good robustness of defending JPEG compression, the algorithm also can be used to locate the median filtering area. The approach achieves much better performance than the existing state-of-the-art methods with different format and size of image blocks, particularly when the image blocks are tiny and have high JPEG compression ratio.

Performance comparison of image feature detectors utilizing a large number of scenes

The availability of a large number of local invariant feature detectors has rendered the task of evaluating them an important issue in vision research. However, the maximum number of scenes utilized for performance comparison has so far been relatively small. This paper presents an evaluation framework and results based on it utilizing a large number of scenes, providing insights into the performance of local feature detectors under varying JPEG compression ratio, blur, and uniform light changes.

Remote Authentication via Biometrics: A Robust Video-Object Steganographic Mechanism Over Wireless Networks

In wireless communications, sensitive information is frequently exchanged, requiring remote authentication. Remote authentication involves the submission of encrypted information, along with visual and audio cues (facial images/videos, human voice, and so on). Nevertheless, Trojan horse and other attacks can cause serious problems, especially in the cases of remote examinations (in remote studying) or interviewing (for personnel hiring). This paper proposes a robust authentication mechanism based on semantic segmentation, chaotic encryption, and data hiding. Assuming that user X wants to be remotely authenticated, initially X’s video object (VO) is automatically segmented, using a head-and-body detector. Next, one of X’s biometric signals is encrypted by a chaotic cipher. Afterwards, the encrypted signal is inserted to the most significant wavelet coefficients of the VO, using its qualified significant wavelet trees (QSWTs). QSWTs provide both invisibility and significant resistance against lossy transmission and compression, conditions that are typical of wireless networks. Finally, the inverse discrete wavelet transform is applied to provide the stego-object. Experimental results regarding: 1) security merits of the proposed encryption scheme; 2) robustness to steganalytic attacks, to various transmission losses and JPEG compression ratios; and 3) bandwidth efficiency measures indicate the promising performance of the proposed biometrics-based authentication scheme.

Watermarking Technique for Improved Management of Digital Medical Images

Although digital medical imaging can improve diagnostic accuracy, efficient data man- agement and protection of patient privacy are major concerns. For example, an inadvertently modified medical image or a disordered series of medical images such as successive images of a beating heart may result in a misdiagnosis. The optimal use of digital medical images should timely detection of medical events while minimizing misdiagnoses and pro- tecting patient privacy. Improved detection of abnormal medical events would also improve the safety of patient care delivery models. We use a watermarking technique to embed im- portable patient data such as identity (ID), serial number or region of interest in the back- ground of a medical image to ensure that the image is matched with the correct patient and to indicate its relationship to other images in a series. The proposed method can be used to embed information at JPEG compression ratios as high as 70%

Image Matching Algorithm based on Feature-point and DAISY Descriptor

Image matching technology is the research foundation of many computer vision problems, and the matching algorithm based on partial features of images is a research focus in this field. In order to overcome the unstable performance of classic SURF algorithm on rotation invariance, an image matching algorithm combined with SURF feature-point and DAISY descriptor is proposed. Based on the feature point detection of SURF algorithm, a principal direction distribution method for DAISY descriptor is put forward, and a novel DAISY descriptor is obtained according to the rotation of the principal direction. In this paper, our proposed algorithm, on the basis of slight increase in running time, improves the image matching capability of the classic SURF algorithm on image rotation. The experimental results show that our proposed algorithm has stronger robustness in a variety of complex cases, such as image blurring, illumination variation, JPEG compression ratio variation, field of view variation, etc. Our proposed algorithm can not only keep the merits of the original SURF algorithm on computation speed, but also improve the matching accuracy on rotation invariance.

Digital image forgery detection using JPEG features and local noise discrepancies.

Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.

Video-Object Oriented Biometrics Hiding for User Authentication under Error-Prone Transmissions

An automatic video-object oriented steganographic system is proposed for biometrics authentication over error-prone networks. Initially, the host video object is automatically extracted through analysis of videoconference sequences. Next, the biometric pattern corresponding to the segmented video object is encrypted by a chaotic cipher module. Afterwards, the encrypted biometric signal is inserted to the most significant wavelet coefficients of the video object, using its qualified significant wavelet trees (QSWTs). QSWTs provide both invisibility and significant resistance against lossy transmission and compression, conditions that are typical in error prone networks. Finally, the inverse discrete wavelet transform (IDWT) is applied to provide the stego-object. Experimental results under various losses and JPEG compression ratios indicate the security, robustness, and efficiency of the proposed biometrics hiding system.

Image authentication using generalised finite automata

An effective technique for combined fragile and semi-fragile image authentication is presented. The authentication signature is extracted from image representation in the automata domain, unlike most conventional methods that are based on frequency domain. In the automata domain, the image is represented in terms of its perceptual self-similarity and can therefore survive high JPEG compression ratios. This method is also capable of changing the degree of fragility by setting the value of the image self-similarity parameter α. Furthermore, the proposed technique features an extremely compact signature length while maintaining a rather simple implementation. Experimental results are presented to demonstrate the effectiveness of the proposed technique.

Digital echocardiography and telemedicine applications in pediatric cardiology.

Digital echocardiography offers several advantages over videotape, including easy review, comparison, storage, postprocessing, and sharing of studies, quantitative analysis, and superior resolution. Newer echocardiography systems can write digital data to computer hardware, whereas older systems require digitization of analog data. Clinical and digital data compression is required to reduce study size. Clinical compression has been validated in several adult studies and one pediatric study. JPEG and MPEG digital compression ratios of 26:1 and 200:1, respectively, approximate S-videotape quality. JPEG is the DICOM 3.0 standard and is ideal for short loops, serial comparisons, and quantitative analysis. MPEG (the motion picture standard) lends itself to digitization of video streams and may be more attractive to pediatric cardiologists. Options for data storage and transfer range from limited local review to multiple offline review stations linked by a wide-area network. Telemedicine expands the capabilities of digital echocardiography in a "store and forward" or "real-time" format. Real-time neonatal telecardiology is accurate, impacts patient care, is cost-effective, and does not increase utilization. Cost, increased reliance on sonographers' skills, lack of accepted standards, and legal, licensure, and billing issues are obstacles to widespread acceptance of digital echocardiography and telemedicine.

Diagnosis of glaucoma using telemedicine--the effect of compression on the evaluation of optic nerve head cup-disc ratio.

A photograph of the optic nerve head requires a lot of disk space (over 1 MByte) for storage and may require substantial bandwidth and time for transmission to a remote practitioner for a second opinion. To test whether compression degrades the image quality of the images, 302 slides were digitized at an optical resolution of 2400 pixels/inch (945 pixels/cm) and 30 bit/pixel. The images were saved both in non-compressed TIFF format and in compressed JPEG (compression ratio of 60) format. A blinded observer measured the optic nerve head cup-disc ratio for all three groups: the original slides, uncompressed TIFF and compressed JPEG images. The results showed that digital images were less accurate than slides. However, compression, even up to a ratio of 40, did not make matters worse.