Algorithm for fractional multiple image enlargement based on all phase DCT

This paper proposes a novel hierarchical coding algorithm based on the All Phase IDCT (APIDCT) interpolation. The All Phase Digital Filter (APDF) is a new type of linear phase filter. For a data vector with a length of N obtained by blocking a signal, there are N different phase data blocks that include the same sampling point. Through taking the mean of the N values as the filtering output, the APDF can eliminate different meanings of the orthogonal transform filtering values of those data vectors and thereby the block effect. According to this idea, this paper deduces the formula of two-dimension APIDCT. Then, this paper compares the performances of several kinds of APDF and demonstrates that the APIDCT filter has the best performance in image interpolation. By combining the interpolation with multi-subsampling and adaptive arithmetic coding, a simple hierarchical image-coding algorithm is shaped up. This technique can be used to scalable coding in spatial resolution. The simulation results show that the compression ratio and restored image quality better than JPEG compression can be achieved by only three layers, and no block effect has been found even in high compression ratio.

The blocking effect is the major drawback in discrete cosine transform (DCT)-based codecs at low bit rates. When a high compression ratio is required, boundaries between adjacent image blocks become visible. This kind of degradation probably will affect the judgment of an end user. In this work, a four-neighboring-block zero-masking technique is proposed in the DCT frequency domain. The proposed algorithm uses a shift block within four adjacent DCT blocks to reduce computational complexity. By slightly modifying several DCT coefficients in the shift block, the artifacts that result from quantization and dequantization processes are alleviated. The simulation results show that both visual perception and objective image quality are noticeably improved.

The aim of image fusion is to combine similar information from multiple images into a single image. The methods which are based on discrete cosine transform (DCT) of image fusion are more competent and time-saving in real-time systems using DCT based standards of still Image. The existing DCT based methods are suffering from some side effects like blurring which can reduce the quality of the output image. To address this issue, the paper proposing new method for image fusion using Gaussian pyramid in DCT domain. The pyramid fusion provides better fusion quality. The execution time is extremely reduced, compare with existing methods. This method can be used for multi model image fusion as well as fusion of complementary images. The algorithm given in proposed system is simple and easy to implement. Also, it could be used for real time applications. The performance of our method is analyzed and compared with other image fusion methods. Experimental results show that there is no difference between the result of our method and water-based image fusion result. But our algorithm is carried out in DCT domain; it is efficient in processing time and simple.

Aim and Objective:The objective of this study was to optimize the threshold for discrete cosine transform (DCT) coefficients for near-lossless compression of Tc-99 m Dimercaptosuccinic acid (DMSA) scan images using discrete cosine transformation.Materials and Methods:Two nuclear medicine (NM) Physicians after reviewing several Tc-99 m DMSA scan images provided 242 Tc-99 m DMSA scan images that had scar. These Digital imaging and communication in medicine (DICOM) images were converted in the Portable Network Graphics (PNG) format. DCT was applied on these PNG images, which resulted in DCT coefficients corresponding to each pixel of the image. Four different thresholds equal to 5, 10, 15, and 20 were applied and then inverse discrete cosine transformation was applied to get the compressed Tc-99 m DMSA scan images. Compression factor was calculated as the ratio of the number of nonzero elements after thresholding DCT coefficients to the number of nonzero elements before thresholding DCT coefficients. Two NM physicians who had provided the input images visually compared the compressed images with its input image, and categorized the compressed images as either acceptable or unacceptable. The quality of compressed images was also assessed objectively using the following eight image quality metrics: perception-based image quality evaluator, structural similarity index measure (SSIM), multiSSIM, feature similarity indexing method, blur, global contrast factor, contrast per pixel, and brightness. Pairwise Wilcoxon signed-rank sum tests were applied to find the statistically significant difference between the value of image quality metrics of the compressed images obtained at different thresholds and the value of the image quality metrics of its input images at the level of significance = 0.05.Results:At threshold 5, (1) all compressed images (242 out of 242 Tc-99 m DMSA scan images) were acceptable to both the NM Physicians, (2) Compressed image looks identical to its original image and no loss of clinical details was noticed in compressed images, (3) Up to 96.65% compression (average compression: 82.92%) was observed, and (4) Result of objective assessment supported the visual assessment. The quality of compressed images at thresholds 10, 15, and 20 was significantly better than that of input images at P < 0.0001. However, the number of unacceptable compressed images at thresholds 10, 15, and 20 was 6, 38, and 70, respectively.Conclusions:Up to 96.65%, near-losses compression of Tc-99 m DMSA images was found using DCT by thresholding DCT coefficients at a threshold value equal to 5.

This paper proposes an image magnification method “IM‐GPDCT,” which is an iterative application of the Gerchberg‐Papoulis (GP) algorithm with the discrete cosine transform (DCT), and its performance is evaluated. Conventional image magnification by interpolation has a problem in that degradation of image quality is inevitable since essentially it is impossible to restore the spatial high‐frequency components which are lost in the observation process. For this point, IM‐GPDCT improves image quality of a magnified image by utilizing a concept to restore the spatial high‐frequency components which are lost in the observation.IM‐GPDCT uses the GP algorithm as the basic principle for extending the frequency band. The spatial high‐frequency components are restored during the forward and inverse iterative transform process for the image by DCT, using two constraints that the spatial extent of an image is finite, and correct information is already known for the low‐frequency components. The proposed method is compared to three conventional interpolation methods from the viewpoints of a restoration error and image quality. Restoration error performance results show that IM‐GPDCT outperforms the interpolation methods from the viewpoint of the restoration error. Simulation results show also that the presented method improves the image quality of the magnified image by obtaining image sharpness, nonjagged edges and reproduction of the original texture.

A lot of attentions has been paid by the researchers for lossy image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality, is still a research issue. In this paper, we proposed an image compression using adaptive scanning, in which DWT(discrete wavelet transform) is performed to source image, DCT(discrete cosine transform) is performed to obtained DWT elements. Next, non zero quantized DCT (discrete cosine transform) coefficients are represented in less number of bits. First, the source image is divided into \(16\times 16\) grids and then DWT transformed, later the obtained elements are divided into \(8\times 8\) grids and then DCT is performed, quantized, differential encoded and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore the indexed vector is divided into two indexed vectors namely \(nonzero\) vector and zerocount vector. The nonzero coefficients are stored in \(nonzero\) vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store highest value of nonzero coefficient in \(nonzero\) vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods.

The Discrete Cosine Transform (DCT) is one of the most widely used techniques of transforms in digital signal processing. It is the main algorithm in image and video coding systems. In this paper, we propose an algorithm which generates enhanced Cordic based Loeffler DCT architectures for angle’s precision degrees ranging from 10−1 to 10−7. High level PSNR, area and power estimators have been proposed to make a trade-off between consumption and image quality. An optimal architecture has been retained for its low complexity, low power and high PSNR. The complexity of this architecture is the lowest among the conventional DCT architectures even the BinDCT which is a reference in terms of reduced complexity. The selected architecture has also the closest PSNR to the reference Loeffler-DCT architecture without a substancial loss of power.

Fourier transform methods are usually adopted to fit 2D closed curves representing samples’ profiles to be studied in morphometric analysis. As for problems concerning 3D open curves, landmark-based methods are widely used. In this paper, a parametric method based on discrete cosine transform (DCT) is proposed to serve as a morphometric tool for 3D open curves. DCT transforms real signal (coordinates) into a combination of cosine functions. DCT describes the shape of a curve with coefficients generated from the fitting curve. Four examples are introduced to be fitted with DCT. The first example is 3D spiral curves with different shapes, added random disturbances to make this model more general. A curve alignment is also utilized to eliminate the non-shape effect. The other three examples of suture curves abstracted from 3D human skulls on which semilandmarks and landmarks are aligned with General Procrustes Analysis (GPA) to eliminate the effect brought by location, size, and orientation. These 3D curves with different diagnoses are matched with DCT. Coefficients generated in the fitting result are analyzed with between-group principal component analysis (bgPCA) and one-way permutational multivariate analysis of variance (PERMANOVA). Different groups of four examples are separated and present significant differences in the results of one-way PERMANOVA. Statistical analyses demonstrate that DCT is promising in morphometric analysis of 3D open curves.

This paper describes the band-splitting properties of the discrete cosine transform (DCT). The one-dimensional DCT, viewed as a filter, can split the filtered output into two halves, one corresponding to the low-frequency band of the input and the other to the high-frequency band of the input. In a two-dimensional case, the DCT behaves like a quadrature mirror filter. Based on the band-splitting properties of the DCT, a dual-mode image compression method has emerged. This new method is used to quantize and encode the image edges in the spatial domain and the uniform image portions in the DCT frequency domain. Analysis and simulation are included herein. Improvement of image quality by the use of this new method has been demonstrated through simulation.

A new derivation for the lapped orthogonal transform (LOT) is presented. It has basis functions that overlap adjacent blocks and decay smoothly to zero, and so it can reduce the blocking effect to very low levels, without any data overhead. It is shown that a quasioptimal LOT can be computed with an algorithm based on the discrete cosine transform (DCT), with only 30% more computation than the DCT. Unlike earlier approaches to the reduction of blocking effects, there is no penalty in coding gain for the LOT, which actually increases the coding gain by 0.25 dB for a first-order Markov process with rho =0.95. >

Image up-sampling in the discrete cosine transform (DCT) domain is a challenging problem because DCT coefficients are de-correlated, such that it is nontrivial to estimate directly high-frequency DCT coefficients from observed low-frequency DCT coefficients. In the literature, DCT-based up-sampling algorithms usually pad zeros as high-frequency DCT coefficients or estimate such coefficients with limited success mainly due to the nonadaptive estimator and restricted information from a single observed image. In this paper, we tackle the problem of estimating high-frequency DCT coefficients in the spatial domain by proposing a learning-based scheme using an adaptive k-nearest neighbor weighted minimum mean squares error (MMSE) estimation framework. Our proposed scheme makes use of the information from precomputed dictionaries to formulate an adaptive linear MMSE estimator for each DCT block. The scheme is able to estimate high-frequency DCT coefficients with very successful results. Experimental results show that the proposed up-sampling scheme produces the minimal ringing and blocking effects, and significantly better results compared with the state-of-the-art algorithms in terms of peak signal-to-noise ratio (more than 1 dB), structural similarity, and subjective quality measurements.

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. Image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

Precise realization of one-staged 2-D DCT using analog current mode architecture in compressed sensing front-end

Generally, at the transmitter video intra frame is sub-sampled by resizing the frame. It is very important to generate good quality high resolution frame from the receiver low resolution frame. The existing most of all up-sampling technique produce undesirable blurring and ringing artefacts. To overcome this problem, a novel technique is proposed here which use both space and frequency domain information. The proposed method use low frequency DCT (Discrete cosine transform) component to sub-sample the frame or image at the transmitter side. In transmitter side a preprocessing method is proposed where the received sub-sampled frame or image is passed though a Wiener filter which uses its local statistics in 3×3 neighbourhood to modify pixel values. The output of Wiener filter is added with optimized multiple of high frequency component. The output is then passed through a DCT block to up-sample. Result shows that the proposed method outperforms most of the widely used existing interpolation techniques in terms of objective and subjective measures.

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.