Blind Separation of Tampered Image Based on JPEG Double Quantization Effect
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The advancement in mobile communication and technologies has led to the usage of short-form digital content increasing daily. This short-form content is mainly based on images that urged the joint photographic experts' group (JPEG) to introduce a novel international standard, JPEG Snack (International Organization for Standardization (ISO)/ International Electrotechnical Commission (IEC) IS, 19566-8). In JPEG Snack, the multimedia content is embedded into a main background JPEG file, and the resulting JPEG Snack file is saved and transmitted as a .jpg file. If someone does not have a JPEG Snack Player, their device decoder will treat it as a JPEG file and display a background image only. As the standard has been proposed recently, the JPEG Snack Player is needed. In this article, we present a methodology to develop JPEG Snack Player. JPEG Snack Player uses a JPEG Snack decoder and renders media objects on the background JPEG file according to the instructions in the JPEG Snack file. We also present some results and computational complexity metrics for the JPEG Snack Player.
JPEG
Lossless JPEG
Image file formats
File size
JPEG 2000
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The 93rd JPEG meeting was held online from 18 to 22 October 2021. The JPEG Committee continued its work on the development of new standardised solutions for the representation of visual information. Notably, the JPEG Committee has decided to release a new call for proposals on point cloud coding based on machine learning technologies that targets both compression efficiency and effective performance for 3D processing as well as machine and computer vision tasks. This activity will be conducted in parallel with JPEG AI standardization. Furthermore, it was also decided to pursue the development of a new standard in the context of the exploration on JPEG Fake News activity.
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JPEG 2000
Lossless JPEG
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Optimizing JPEG quantization tables is expected to transmit or store JPEG images. The Hamiltonian algorithm is one optimizing method and it can simultaneously optimize numerous parameters. JPEG quantization tables consists of 128 variables. We propose applying the Hamiltonian algorithm for optimizing JPEG quantization tables. The JPEG quantization tables, which are optimized by the Hamiltonian algorithm, can reduce the rate and improve the quality of a decoded JPEG image. This report describes a configuration of a JPEG quantization table optimizing system and the experimental result of using it.
JPEG
Lossless JPEG
Hamiltonian (control theory)
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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.
JPEG
Lossless JPEG
Lossy compression
JPEG 2000
ENCODE
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A recursive algorithm is presented for generating quantization tables in JPEG (Joint Photographic Experts Group) baseline coders from the actual statistics of the input image. Starting from a quantization table with large step sizes, corresponding to low bit rate and high distortion, one entry of the quantization table is updated at a time so that, at each step, the ratio of decrease in distortion to increase in bit rate is approximately maximized. This procedure is repeated until a target bit rate is reached. Simulation results demonstrate that, with picture-adaptive quantization tables designed by the proposed algorithm, the JPEG DCT (discrete cosine transform) coder is able to compress images with better rate-distortion performance than that achievable with conventional empirically designed quantization tables.< >
JPEG
Rate–distortion theory
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Image quality manipulating in JPEG is done by quantization tables. JPEG has two quantization tables – one table for the luminance information and one table for the chrominance information. These quantization tables have been designed in support of images with few sharp changes; however, typically most GPS image maps have many sharp changes and as a result, the images are not optimally compressed. The designers of the quantization tables have presumed that sharp changes in the colors will rarely occur. Therefore, they divide the values that represent sharp changes in the frequency space by large numbers and divide other values by smaller numbers. As a result, when there are sharp changes in an image, the proportional allocation for each kind of data in the compressed image is inappropriate and results in an inefficient compression. In this paper the standard quantization tables have been modified as to handle the different kinds of GPS image map data appropriately. Consequently, the experimental results show that images with sharp changes are compressed more efficiently when making use of the new quantization tables.
JPEG
Chrominance
Trellis quantization
Compression artifact
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The new standard of the JPEG community, called the JPEG XR, carries the features of its earlier standards, namely the JPEG Baseline and the JPEG 2000. JPEG Baseline and JPEG 2000 have been in extensive use since their introduction; and have pre-defined tools in various platforms, such as MATLAB, for simulation and testing purposes. However, the same doesn't apply to the latest JPEG XR standard and thus creates a barrier in the ease of use for the global community. JPEG XR employs photo core transform for still-image compression, and in this paper, we propose a novel software implantation of the forward and the inverse photo core transform, PCT and IPCT respectively. The implementations are lifting-based operations that can be made multiplier-less for hardware simplicity.
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Lossless JPEG
JPEG 2000
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Over the past decade, convolutional neural networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks. They can learn robust representations of image data by processing RGB pixels. Since image data are often stored in a compressed format, from which JPEG is the most widespread, a preliminary decoding process is demanded. Recently, the design of CNNs for processing JPEG compressed data has gained attention from the research community. They process DCT coefficients instead of RGB pixels, saving computation for decoding JPEG images, however, at the cost of increasing the computational complexity of the network. In this paper, we examine how spatial resolution and JPEG quality impacts on the performance of a state-of-the-art CNN designed to operate directly on the JPEG compressed domain. To alleviate its computational complexity, we propose a Frequency Band Selection (FBS) technique to select the most relevant DCT coefficients before feeding them to the network. Experiments were conducted on a subset of the ImageNet dataset considering both fine- and coarse-grained image classification tasks. Results show that such networks are resilient to JPEG quality but are susceptible to spatial resolution. Also, our FBS can reduce the computational complexity of the network while retaining a similar accuracy.
JPEG
RGB color model
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Image formats specified by the joint photographic expert group (JPEG) are preferred for images with high colour content. Along with graphic interchange format (GIF) images, JPEG is the most commonly used image format on the Internet and JPEG is the de facto still image format for digital cameras. All major digital camera manufacturers use JPEG as its exclusive or primary still image format. Most JPEG processing occurs in software programs like Microsoft Paint and Adobe Photoshop. Relatively few hardware solutions exist for processing JPEG images. This paper presents a comprehensive literature survey of hardware solutions for JPEG images. Wherever possible, different JPEG formats and accompanying hardware are presented and compared to illustrate various advantages/disadvantages. The performance of hardware and software for JPEG is compared and an overview of commercial hardware for JPEG is also provided
JPEG
Lossless JPEG
Image file formats
JPEG 2000
Digital Imaging
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In this paper, two schemes for optimizing the JPEG quantizer are investigated. The first scheme starts from a given quantization-table and executes a sub-optimal search for quantization-table parameters. Those parameters, when changed by a pre-specified incremental step-size, result in the most optimal move towards decreasing (increasing) the compressed file-size, with a minimal increase (maximum decrease) of error. This procedure is repeated, until a pre- specified file-size is reached. The second scheme is based on performing a mapping from the JPEG default quantization- table to an optimized one. This mapping adapts the default quantization-table to the statistics of the DCT coefficients of the image at hand. The superiority of these two optimized JPEG quantizers is established in terms of the visual quality of their reconstructed images. This was done by running comparative visual image quality experiments involving 20 human subjects. These optimized quantizers were also demonstrated to result in a higher level of machine image quality, by improving the accuracy of cheque amount reading applications. Experimental results indicate that the second optimization scheme yields a higher level of visual image quality, while requiring a fraction of the processing time used by the first scheme.
JPEG
File size
Lossless JPEG
Table (database)
Image file formats
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