DCT-based JPEG Research Articles

A novel non-customary method of image compression based on image spectrum

Compression of multimedia content is an important processing step and backbone of real life applications in terms of optimum resource utilization in transmission and storage. It is an established field of research with very little scope for further improvement in achieved compression through customary coding-based compression techniques. Consequently, non-customary compression methods have become an important area for future research. Based on the principle ‘Any information that can be restored can be compressed’, we propose a novel spectrum-based image compression technique to further reduce the data footprint with satisfactory quality metric for images. We first blur the image with a point spread function (PSF) determined using frequency content of the given image. Blurring increases the DC component in the image, which in turn gets further compressed compared with original image by DCT-based JPEG compression. To recover the image, we perform deconvolution using the known blur PSF. Results obtained show further improvement of $$20-30\%$$ in achieved compression with respect to original JPEG compressed image with satisfactory quality of recovered image.

Efficiency of adaptive fractal image compression with archetype classification and its modifications

The DCT-based JPEG image compression technique is a standard for lossy image compression. But the technique is not suitable at high compression ratios. Again, it is resolution dependent. Alternative compression techniques are introduced to reduce these problems. One such popular technique is fractal-based image compression. In this paper, efficiency of a fractal-based image compression technique is discussed that applies adaptive quadtree partitioning and archetype classification schemes. The adaptive quadtree partitioning scheme is dependent on image pixel context to share some self-similar structure of the image and improves the decoded image quality significantly. The archetype classification scheme tries to base the technique on finding better covering of range by domain that suites better in fractal image compression techniques. Then, modification of the same is also done to enhance the performance by applying dictionary-based loss-less data compression techniques, i.e. OLZW on the fractal compressed image that is basically a collection of affine transforms. The affine transform consists of a number of parameters that are compressed using OLZW technique. After that, the same experiment is carried out by a variant of OLZW, i.e. MOLZW. The experimental results are compared among existing and modified techniques. The modified versions yield not only better image quality but also high encoding speed than their counterparts.

Mathematical Relation between APBT-Based and DCT-Based JPEG Image Compression Schemes

Mathematical Relation between APBT-Based and DCT-Based JPEG Image Compression Schemes

Windowed All Phase Biorthogonal Transform and Its Application in JPEG-Like Image Compression

This paper proposes new concepts of the windowed all phase biorthogonal transform (WAPBT), which is inspired by the all phase biorthogonal transform (APBT). In the light of windowed all phase digital filter theory, windowed all phase biorthogonal transforms is proposed. The matrices of WAPBT based on DFT, WHT, DCT and IDCT are deduced, which can be used in image compression instead of the conventional DCT, and the image compression scheme proposed in this paper is called WAPBT-based JPEG (WAPBT-JPEG). With optimal window sequence of WAPBT for image compression obtained by using generalized pattern search algorithm (GPSA), the peak signal to noise ratio (PSNR) and visual quality of the reconstructed images using the WAPBT-JPEG is outgoing DCT-based JPEG (DCT-JPEG) and APBT-based JPEG (APBT- JPEG) approximately at all bit rates. What is more, by comparison with DCT-JPEG, the advantage of proposed scheme is that the quantization table is simplified and the transform coefficients can be quantized uniformly. Therefore, the computing time becomes shorter and the hardware implementation is easier. Index Terms—Windowed all phase biorthogonal transform (WAPBT), image compression, discrete cosine transform (DCT), JPEG algorithm, generalized pattern search algorithm (GPSA), windowed all phase digital filter (APDF)

APBT-based Image Coding at Low Bit Rates with Down-sampling and All Phase DCT Interpolation

The conventional JPEG standard adopts the Discrete Cosine Transform (DCT), which usually needs the more complex quantization table and adjusting the bit rate results more complex multiplications. Based on the theory of All Phase Biorthogonal Transform (APBT), this paper proposed a novel image coding algorithm based on down-sampling, APBT-based image compression (APBT-JPEG) and interpolation. In the algorithm, down-sampling to the image data after the All Phase DCT (APDCT) filtering before compression and subsequently interpolation to the decompressed image are applied and especially APBT is used in the image compression step instead of the DCT. In the interpolation step, cubic convolution interpolation and the APDCT interpolation were used respectively. Experimental results show that the proposed scheme outperforms the conventional DCT-based JPEG (DCT-JPEG) compression scheme at low bit rates both in terms of PSNR and visual quality.

Computationally Efficient Formulation of Sparse Color Image Recovery in the JPEG Compressed Domain

Sparse representations provide a powerful framework for various image processing tasks, among which image recovery seems to be an already classical application. While most developments of image recovery applications are focused on finding the best dictionary, the possibility of using already existing sparse image representations tends to be ignored. This is the case of the JPEG compressed image representation, which is a sparse image representation in terms of the discrete cosine transform (DCT) dictionary. The development of sparse frameworks directly on the JPEG encoded image representation can lead to computationally efficient approaches. Here we introduce a DCT-based JPEG compressed domain formulation of the color image recovery process within a sparse representation framework and we prove mathematically and experimentally not only its numerical efficiency as compared to the pixel level formulation (the processing time is reduced up to 40 %), but also the good quality of the restoration results.

Increasing image compression rate using steganography

The goal of image compression is to remove the redundancies for minimizing the number of bits required to represent an image while steganography works by embedding the secret data in redundancies of the image in invisibility manner. Our focus in this paper is the improvement of image compression through steganography. Even if the purposes of digital steganography and data compression are by definition contradictory, we use these techniques jointly to compress an image. Hence, two schemes exploring this idea are suggested. The first scheme combines a steganographic algorithm with the baseline DCT-based JPEG, while the second one uses this steganographic algorithm with the DWT-based JPEG. In this study data compression is performed twice. First, we take advantage of energy compaction using JPEG to reduce redundant data. Second, we embed some bit blocks within its subsequent blocks of the same image with steganography. The embedded bits not only increase file size of the compressed image, but also decrease the file size further more. Experimental results show for this promising technique to have wide potential in image coding.

Variable Length Reconfigurable Algorithms and Architectures for DCT/IDCT Based on Modified Unfolded Cordic

A coordinate rotation digital computer (CORDIC) based variable length reconfigurable DCT/IDCT algorithm and corresponding architecture are proposed. The proposed algorithm is easily to extend to the 2n-point DCT/IDCT. Furthermore, we can easily construct the N-point DCT/IDCT with two N/2-pt DCTs/IDCTs based the proposed algorithm. The architecture based on the proposed algorithm can support several power-of-two transform sizes. To speed up the computation of DCT/IDCT without losing accuracy, we develop the modified unfolded CORDIC with the efficient carry save adder (CSA). The rotation angles of CORDIC used in proposed algorithm are arithmetic sequence. For convenience, we develop the architecture of N-point IDCT with the orthogonal property of DCT and IDCT transforms. The proposed architecture are modeled with MATLAB language and performed in DCT-based JPEG process, the experimental results show that the peak signal to noise ratio (PSNR) values of proposed architectures are higher than the existing CORDIC based architectures at both different quantization factors and different test images. Furthermore, the proposed architectures have higher regularity, modularity, computation accuracy and suitable for VLSI implementation.

WDCT-based Image Coding Algorithm with Down-Sampling and APDCT Interpolation

Abstract The DCT-based JPEG standard is the most commonly used lossy compression algorithm for still continuous-tone images due to its high effectiveness and low computational complexity. However, the visually disturbing blocking artifacts generate when image is compressed at low bit rates. With the frequency warping, the warped discrete cosine transform (WDCT) makes the signal energy distribution more suitable for image coding. In order to improve the coding performance at low bit rates, this paper presents an image coding frame based on down-sampling and all phase DCT (APDCT) interpolation. Experimental results show that, no matter for PSNR or subjective result, the proposed method obviously improves the performance of image coding at low bit rates, and reduces the blocking artifacts significantly.

All phase biorthogonal transform and its application in JPEG-like image compression

This paper proposes new concepts of the all phase biorthogonal transform (APBT) and the dual biorthogonal basis vectors. In the light of all phase digital filtering theory, three kinds of all phase biorthogonal transforms based on the Walsh transform (WT), the discrete cosine transform (DCT) and the inverse discrete cosine transform (IDCT) are proposed. The matrices of APBT based on WT, DCT and IDCT are deduced, which can be used in image compression instead of the conventional DCT. Compared with DCT-based JPEG (DCT-JPEG) image compression algorithm at the same bit rates, the PSNR and visual quality of the reconstructed images using these transforms are approximate to DCT, outgoing DCT-JPEG at low bit rates especially. But the advantage is that the quantization table is simplified and the transform coefficients can be quantized uniformly. Therefore, the computing time becomes shorter and the hardware implementation easier.

Progressive content access to databases of JPEG-compressed images

Progressive content access provides a mode that allows a coarse version of an image being viewed at a lower computing cost and then gradually refined by subsequent resolution enhancement if required. This proves extremely useful when millions of compressed images and video sequences need to be browsed manually or processed in pixel domain, saving the cost and removing the necessity of full decompression. Such a progressive content access algorithm suitable for all discrete-cosine-transform-based (DCT-based) JPEG and MPEG-compressed files is proposed here. First a theoretical model in approximation of cosine function used in inverse discrete cosine transform (IDCT) with various orders is developed. Following that a progressive content access algorithm, which comprehends both the successive approximation and the spectral selection, is proposed. Further analysis and experiments are reported to show that our proposed algorithm saves computational cost in comparison with JPEG full decompression. Extensive experiments also support that the proposed algorithm achieves encouraging peak signal-to-noise ratio (PSNR) values for reconstructed images even with lower order approximation.

An effective method for still image compression/decompression for transmission on PSTN lines based on modifications of Huffman coding

This paper presents an effective compression method suitable for transmission the still images on public switching telephone networks (PSTN). Since compression algorithm reduce the number of pixels or the gray levels of a source picture, therefore this will lead to the reduction of the amount of memory needed to store the source information or the time necessary for transmitting by a channel with a limited bandwidth. First, we introduced some current standards and finally the lossy DCT-based JPEG compression method is chosen. According to our studies, this method is one of the suitable methods. However, it is not directly applicable for image transmission on usual telephone lines (PSTN). Therefore, it must be modified considerably to be suitable for our purposes. From Shannon’s Information Theory, we know that for a given information source like an image there is a coding technique which permits a source to be coded with an average code length as close as to the entropy of the source as desired. So, we have modified the Huffman coding technique and obtained a new optimized version of this coding, which has a high speed and is easily implemented. Then, we have applied the DCT 1 Discrete Cosine Transform. 1 and the FDCT 2 Forward DCT. 2 for compression of the data. We have analyzed and written the programs in C++ for image compression/decompression, which give a very high compression ratio (50:1 or more) with an excellent SNR. 3 Signal to Noise Ratio. 3 In this paper, we present the necessary modifications on Huffman coding algorithms and the results of simulations on typical images.

Compact description of 3-D image gamut by <bold>r</bold>-image method

We propose a compact description method for image gamut shell shapes. The 3-D image-to-device gamut mapping algorithm (I-D GMA) is an ideal way to map a display image into the inside of a printer gamut, keeping the loss in color information to a minimum. The key point in I-D GMA is to make use of the relation in image and device gamut boundaries along the mapping line. The image gamut surface is shaped into polygon meshes by extracting the most outside points from the random color distributions in the images. A compact gamut boundary descriptor (GBD) attached to the image data is used for executing the I-D GMA flexibly on the user side. In our proposal, the image gamut shell shape is compactly represented by a 2-D monochromatic image, called the r image. Each pixel in the r image denotes the maximum radial vector magnitude extracted from the subdivided segment in discrete polar angle color space. Since the r image is highly correlated spatially when the image has a smooth gamut shell surface, it is easily compressed by applying the conventional transform coding techniques such as the discrete cosine transform (DCT), singular value decomposition, (SVD) or wavelet. The compressed ultra-compact r image is delivered to the users attached to the image data and is used for I-D gamut mapping. We present how the complicated 3-D image gamut shell shape is simply described by the 2-D monochromatic r image and easily reconstructed with its surface colors. In practice, wavelet coding was most efficient in compressing the r image as compared with a DCT-based JPEG. It is shown that the complex shell shapes are well reconstructed from around 400 bytes of compact data compressed by SVD or less by a wavelet.

An Advanced Color Representation for Lossy Compression of CMYK Prepress Images

CMYK color images are used extensively in prepress applications. When compressing those color images one has to deal with four different color channels. Usually compression algorithms only take into account the spatial redundancy that is present in the image data. This approach does not yield an optimal data reduction since there also exists a high correlation between the different colors in natural images. This paper shows that a significant gain in data reduction can be achieved by exploiting this color redundancy. Some popular transform coders, including DCT-based JPEG and the SPIHT wavelet coder, were used for reducing the spatial redundancy. The performance of the algorithms was evaluated using a quality criterion based on human perception like the mean CIEL*a*b*ΔE error.

Audio (data) on image DCT-based JPEG codec

This article examines an original process to code signals from two different sources (data e.g. audio- and video) mixed together, and coded within a single coding process (DCT-based JPEG codec), in order to generate a single data stream at its output, instead of two as in the usual multimedia applications.

Software JPEG for a 32-bit MCU with dual issue

This paper describes a new software solution for DCT-based JPEG image compression using a 32-bit microcontroller unit (MCU) with parallel execution (dual issue). Taking the constraints of the MCU hardware resources into consideration, various fast algorithms for DCT are investigated. As a result, it is concluded that the optimal fast algorithm for MCUs capable of parallel execution differs from the optimal algorithm for MCUs which do not support parallel execution. Comparing the number of JPEG image compression cycles using software on MCUs with and without parallel execution capability, it is concluded that the processing speed of the latter type of MCU is approximately 30% higher than the former type. When this former processing speed is applied to a 640/spl times/480 (VGA-size) YCbCr image in 4:2:2 format, compression can be performed in about 0.15 seconds (using a 100 MHz MCU with parallel execution capability).

JPEG software solution for a 32-bit MCU

New software solution for DCT-based JPEG image compression is proposed. The solution make effective use of RISC architecture and quantized DCT data domain representation. As a result, 640/spl times/480 (VGA size) YCbCr images in the 4:2:2 format can be encoded and decoded in about 0.5 of a second by using a 32-bit MCU and DRAM.