Rate-constrained picture-adaptive quantization for JPEG baseline coders
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Abstract:
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.< Keywords:
JPEG
Rate–distortion theory
Video steganography is a data-hiding technology that inserts confidential messages intended to be hidden in video data at an unrecognizable level. Image steganography can be divided into spatial area techniques for inserting confidential data by manipulating the Last Significant Bit (LSB) of each image pixel and frequency domain techniques for manipulating the DCT (Discrete Cosine Transform) coefficients of images. JPEG (Joint Photographic Experts Group), which is used as the image loss compression standard, uses JPEG steganography techniques such as Jsteg, F3, F4, and F5 based on frequency domain techniques that manipulate DCT coefficients. Each technique has the characteristic that the frequency of DCT coefficient of 0 increases compared to the original. Various techniques are presented to determine JPEG Stego images by machine learning the characteristics of the relationship between image complexity and frequency of DCT coefficient 0. Therefore, this paper proposes a JPEG steganography algorithm to prevent the determination of the Stego image using DCT coefficient 0 and maintain an even coefficient distribution based on the JPEG characteristic, DCT coefficient 0.
Steganalysis
JPEG
Steganography tools
Lossless JPEG
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An efficient discrete cosine transform image coding system using the gain/shape vector quantizers (DCT-G/S VQ) is presented. In the coding system, AC transform coefficients in a subblock are partitoned into several bands according to the Schaming's method, and the normalized AC transform coefficients of each band are quantized with the gain/shape vector quantizer designed on a spherically symmetric probability model. In addition, an adaptive DCT-G/S VQ (A-DCT-G/S VQ) is presented by incorporating a modification of the recursive quantization technique in the DCT-G/S VQ. The coding systems are simulated on color images, and their performance is compared to that of previously reported discrete cosine transform coding systems using the Max-type scalor quantizers.
Lapped transform
Sub-band coding
Coding gain
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The paper presents a technique for image compression using the Discrete Cosine Transform (DCT) method. In the Joint Photographic Expert Group norm (JPEG), the image is usually compressed using an "universal" quantization matrix. We propose a technique which employs an appropriate distribution model of the DCT coefficients to deduce the quantization matrix from a set of training images. This technique compared to the classical JPEG gave no blocking effect at the same compression rate.< >
JPEG
Trellis quantization
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JPEG
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Steganalysis
JPEG
Steganography tools
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JPEG
Lossless JPEG
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JPEG
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The discrete cosine transform (DCT) is now well recognized as one of the most important technique in image data compression. Among the class of transforms possessing fast computational algorithms, the cosine transform has a superior energy compaction property. Due to its simple implementation scheme, the DCT is widely used as a substitute to the optimal Karhunen Loeve transform. In this paper, the discrete cosine transform is presented and an algorithm for its implementation is developed. The picture is firstly transform coded using 8 X 8 sub-blocks then a quantization and an entropy coding are used.
Lapped transform
Discrete sine transform
Discrete Hartley transform
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A two-dimensional discrete cosine transform (2-D DCT), often used for image coding, has been applied to sequences of speech spectra produced by the maximum likelihood method (MLM). The coded data was compressed by nearly 90%, reducing it to a size smaller than that needed to store the coefficients of a 10th order linear predictive coding (LPC) model. The DCT-encoded data was then reconstructed and tested for intelligibility. It was found that the two-dimensional DCT method was significantly more intelligible and more natural-sounding than the LPC technique.
Lapped transform
Intelligibility (philosophy)
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The success in discrete cosine transform(DCT) image coding is mainly attributed to recognition of the importance of data organization and representation. In this paper, we proposed an embedded image coder based on quadtree set partition in DCT domain (EZDCT) which is suitable for many kinds of DCT coefficients reorganization schemes. The experimental results show that it is among the state-of-the-art DCT-based image coders when compared with the famous DCT-based image coders, such as EZDCT and MRDCT. For example, for the Barbara image, EQDCT outperforms JPEG EZDCT and MRDCT by 3.3,1.71,1.70 dB in peak-signal-to-noise ratio at 0.25 bpp, respectively.
Quadtree
JPEG
Trellis quantization
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