Block-based DCT Research Articles

An efficient face recognition system based on embedded DCT pyramid

In this paper, an efficient feature selection method based on a combination of DCT pyramid for image decomposition and the concept of the set partitioning in hierarchal trees (SPIHT) for structuring of information for face recognition is presented. In the proposed method, the DCT pyramid decomposes each face image into an approximation subband and a set of reversed L-shape blocks containing the high frequency coefficients. The generalized parent-child relationships of SPIHT algorithm are then established among the DCT pyramids. This leads to efficient selection of the most important coefficients among the layers of the DCT pyramid. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based SPIHT feature selection. Moreover, it outperforms the other well-known methods such as the Eigenfaces and the block-based DCT with the zigzag scanning structure in terms of both accuracy and memory requirement.

Face Recognition using Block-Based DCT Feature Extraction

Face recognition (FR) with reduced number of features is challenging and energy based feature extraction is an effective approach to solve this problem. However, existing methods are hard to extract only the required low frequency features, which is important for capturing the intrinsic features of a face image. This paper proposes a novel Block-Based Discrete Cosine Transform (BBDCT) for feature extraction wherein each 8x8 DCT block is of adequate size to collect the information within that block without any compromise. Individual stages of FR system are examined and an attempt is made to improve each stage. A Binary Particle Swarm Optimization (BPSO)-based feature selection algorithm is used to search the feature vector space for the optimal feature subset. Experimental results show the promising performance of BBDCT for face recognition on 4 benchmark face databases, namely, ORL, Cropped UMIST, Extended Yale B and Color FERET databases. A significant increase in the overall recognition rate and a substantial reduction in the number of features, are observed.

Reduced-reference image quality assessment in reorganized DCT domain

In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by depicting the subband statistical characteristics in the reorganized discrete cosine transform (RDCT) domain. First, the block-based DCT coefficients are reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For the intra RDCT subband characteristic, the coefficient distribution of each RDCT subband is modeled by the generalized Gaussian density (GGD) function. The city-block distance (CBD) is employed to measure the modeling error between the actual distribution and the fitted GGD curve. For the inter RDCT subband characteristic, the mutual information (MI) is utilized to depict the dependencies between coefficient pairs in related RDCT subbands. Moreover, a frequency ratio descriptor (FRD) calculated in the RDCT domain is proposed to depict how the image energy distributes among different frequency components. The FRD values computed from both the reference and distorted images are jointly considered to derive a novel mutual masking strategy for simulating the texture masking property of the human visual system (HVS). By considering the GGD modeling of intra RDCT subband, MI of inter RDCT subbands, and FRD of the image, the proposed RR IQA is developed. Experimental results demonstrate that a small number of RR features is sufficient to represent the reference image for the perceptual quality analysis. The proposed method can outperform the state-of-the-art RR IQAs, and even the full-reference (FR) PSNR and SSIM.

Blocking Artifacts Reduction in Block based Discrete Cosine Transform Compressed Images

Image compression is a technique to reduce the volume of information to be transmitted. Compression technique reduces the bandwidth and the volume of the information to be transmitted. We can compress audio signal, video signal, text, fax and images. For medical images lossless compression is used and for other types lossy compression can be used. For compressing an image we can use the DCT technique. But after compression during decompression and recovering original image from compressed image we can face problem of blocking artifacts. Various methods can be used for removing blocking artifacts. One of them is DCT filtering. Further for getting better results we can remove blocking artifacts by spatial and hybrid filtering method. Our experimental results shows that hybrid filtering gives better performance on the bases of better PSNR, BER and MSE.

Image deblocking via sparse representation

Image compression based on block-based Discrete Cosine Transform (BDCT) inevitably produces annoying blocking artifacts because each block is transformed and quantized independently. This paper proposes a new deblocking method for BDCT compressed images based on sparse representation. To remove blocking artifacts, we obtain a general dictionary from a set of training images using the K-singular value decomposition (K-SVD) algorithm, which can effectively describe the content of an image. Then, an error threshold for orthogonal matching pursuit (OMP) is automatically estimated to use the dictionary for image deblocking by the compression factor of compressed image. Consequently, blocking artifacts are significantly reduced by the obtained dictionary and the estimated error threshold. Experimental results indicate that the proposed method is very effective in dealing with the image deblocking problem from compressed images.

Content-adaptive deblocking for high efficiency video coding

In this paper, a content-adaptive deblocking method is proposed to improve the visual quality of block-based DCT compressed videos. We find that the edge information obtained through the global orientation energy edge detection (OEED) on an initially deblocked image provides a robust partition of local directional features (LDFs). Based on this partition, for the directional featured region, we design corresponding filter orientation and thresholds to best preserve image details while reducing blocking artifacts; for the consecutive non-featured region, we impose extra smoothing to suppress the visually severe blocking artifacts. Experimental results demonstrate that our method effectively improves the visual quality while well maintaining the objective fidelity of block-based DCT compressed videos, compared with the H.264/AVC deblocking method and other existing directional deblocking methods.

Image fusion scheme based on nonsubsampled contourlet and block-based cosine transform

A kind of nonsubsampled contourlet and block-based cosine transform (NSCBCT) is developed, and its application in image fusion is studied in this paper. The construction of filtering banks is based on the nonsubsampled contourlet transform (NSCT) and block-based discrete cosine transform (B-DCT). We combine NSCT and B-DCT to design filters that lead to NSCBCT with better singularity representation than either of them in isolation. A design framework based on the hybrid approach is proposed, which allows for the fast implementation based on NSCT and B-DCT respectively. In addition, a new image fusion scheme based on NSCBCT for multispectral and panchromatic satellite images is proposed. Firstly, because it adopts NSCT, the fused satellite images have higher spatial resolution than those based on wavelets. Secondly, based on the localized high frequency information provided by B-DCT, the proposed fusion scheme can reduce the spectral distortion of the fused image further. Experimental results show that the proposed fusion method is able to increase the spatial resolution and reduce the spectral distortion of the fused image at the same time.

A Face Recognition System Based on BDIP and DCT Pyramid

In this paper, an efficient feature extraction method based on block difference of inverse probabilities (BDIP) and DCT pyramid for face recognition is proposed. The BDIP is first computed in a face image in order to overcome the variation of illumination. The extracted BDIP image is then decomposed using DCT pyramid. The DCT pyramid decomposes the BDIP image into an approximation subband and a set of reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods and the other well known methods such as the PCA and the block-based DCT with the zigzag scanning.

Compression artifacts removal by signal adaptive weighted sum technique

Many mobile devices compress images excessively to meet limited bandwidth requirements and adopt the Block-based Discrete Cosine Transform (BDCT) coding structure. This produces inevitably the visually annoying noises including blocking artifacts in the decoded images. We present a Signal Adaptive Weighted Sum (SAWS) technique of block boundary pixels to alleviate the blocking artifacts encountered in highly compressed images. The weights are adjusted adaptively according to the directional correlation and activities of local areas. To avoid blurring the original details, we employ a parameter that can control the strength of deblocking. Comprehensive experiments demonstrate that the proposed approach achieves excellent visual quality and PSNR compared to a number of deblocking methods in the literature. Moreover, the low complexity of the proposed algorithm facilitates the application to mobile devices.

Zeros Removal with DCT Image Compression Technique

The discrete cosine transform (DCT) is a method for converting a signal into plain frequency components. It is extensively used in image compression. In this paper a new technique is proposed, namely ZRDCT (Zeros Removal with DCT) which is based on a lossy compression, and used to enhance image data compression.Image quality is measured impartially, using peak signal-to-noise ratio (PSNR) or picture quality scale, and individually using perceived image quality with compression factor (CF) being the main theme of this paper, taking into consideration the preservation of well PSNR outputs. The performance of DCT compression generally degrades low bit-rates mainly because of the underlying block-based DCT scheme. Experimental results demonstrated the effectiveness of the ZRDCT approach, which enhanced the performance of the conventional DCT image compression methods, by investigating and interrogating the whole image and hence enforcing mechanisms for finding possible redundant information and therefore the removal of unnecessary data which lead to an improvement in CF without upsetting PSNR.The new technique also proved to have low distortions with good quality PSNR, commendable CF and good execution time, when compared to other various DCT schemes and with some wavelet based image compression

A signal adaptive filter for blocking effect reduction of JPEG compressed images

One of the major drawbacks of the block-based DCT compression methods is that it may result in visible artifacts at block boundaries due to coarse quantization of the coefficients. This paper proposes a new adaptive post-filtering algorithm to remove coding artifacts in block-based DCT compressed images. Presence of blocking artifacts is detected using block activity based on human visual system and block statistics. The boundary regions between the blocks are identified as smooth, non-smooth or intermediate regions. The blocking artifact in the smooth and non-smooth regions are removed by modifying a few DCT coefficients while an edge preserving smoothing filter is applied to the intermediate region. Experimental results illustrating the performance of proposed method on the basis of PSNR, MOS, MSSIM, SF, and BBM indices are presented and evaluated. Compared with other methods, the proposed one achieves better detail preservation and artifact removal performance with lower computational complexity.

Blocking artefact detection in block-based DCT compressed images

The perceived quality of images reconstructed from low bit rate compression is severely degraded by appearance of transform coding blocking artefacts. It is of interest to be able to numerically assess the degree of blocking artefacts as it plays an important role in the design, optimisation and assessment of image and video coding systems. The proposed algorithm is based on new modelling function for blocking artefact detection in compressed images. Our experiment results show that the proposed method exhibits satisfactory performance as compared to other post-processing method’s/techniques and is very efficient and stable since the signal need not be compressed/decompressed.

Low-memory requirement and efficient face recognition system based on DCT pyramid

Face recognition (FR) is a challenging issue due to variations in pose, illumination, and expression. In this paper, a face recognition system with low-memory requirement and accurate recognition is presented. It is based on extraction of features with the DCT pyramid, in contrast to the conventional method of wavelet decomposition. The DCT pyramid performed on each face image decomposes it into an approximation subband and the reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods. Moreover, it outperforms the other well known methods such as PCA and the block-based DCT with the zigzag scanning in terms of accuracy and memory requirement.

Fast extraction of wavelet-based features from JPEG images for joint retrieval with JPEG2000 images

In this paper, some fast feature extraction algorithms are addressed for joint retrieval of images compressed in JPEG and JPEG2000 formats. In order to avoid full decoding, three fast algorithms that convert block-based discrete cosine transform (BDCT) into wavelet transform are developed, so that wavelet-based features can be extracted from JPEG images as in JPEG2000 images. The first algorithm exploits the similarity between the BDCT and the wavelet packet transform. For the second and third algorithms, the first algorithm or an existing algorithm known as multiresolution reordering is first applied to obtain bandpass subbands at fine scales and the lowpass subband. Then for the subbands at the coarse scale, a new filter bank structure is developed to reduce the mismatch in low frequency features. Compared with the extraction based on full decoding, there is more than 72% reduction in computational complexity. Retrieval experiments also show that the three proposed algorithms can achieve higher precision and recall than the multiresolution reordering, especially around the typical range of compression ratio.

A new spread spectrum watermarking method using two levels DCT

In this paper, a discrete cosine transform (DCT) based blind watermarking scheme based on spread spectrum communications is proposed. We perform block-based DCT (BDCT) on the host image; then using the DC coefficients of each block, we construct a low-resolution approximation image. We apply BDCT on this approximation image, then watermark is embedded by adding a pseudo random noise sequence into its high frequencies. In detection stage, we extract the approximation image from the watermarked image, then the same pseudo random noise sequence is generated, and its correlation is computed with high frequencies of the watermarked approximation image. In our method, higher robustness is obtained because of embedding the watermark in low frequencies. In addition, higher imperceptibility is gained by scattering the watermark's bit in different blocks. Compared with related works, our method proved to be highly resistant in cases of many common attacks, while preserving high peak signal to noise ratio for the watermarked images.

A Perceptually Relevant No-Reference Blockiness Metric Based on Local Image Characteristics

A novel no-reference blockiness metric that provides a quantitative measure of blocking annoyance in block-based DCT coding is presented. The metric incorporates properties of the human visual system (HVS) to improve its reliability, while the additional cost introduced by the HVS is minimized to ensure its use for real-time processing. This is mainly achieved by calculating the local pixel-based distortion of the artifact itself, combined with its local visibility by means of a simplified model of visual masking. The overall computation efficiency and metric accuracy is further improved by including a grid detector to identify the exact location of blocking artifacts in a given image. The metric calculated only at the detected blocking artifacts is averaged over all blocking artifacts in the image to yield an overall blockiness score. The performance of this metric is compared to existing alternatives in literature and shows to be highly consistent with subjective data at a reduced computational load. As such, the proposed blockiness metric is promising in terms of both computational efficiency and practical reliability for real-life applications.

Encrypted Domain DCT Based on Homomorphic Cryptosystems

Signal processing in the encrypted domain (s.p.e.d.) appears an elegant solution in application scenarios, where valuable signals must be protected from a possibly malicious processing device. In this paper, we consider the application of the Discrete Cosine Transform (DCT) to images encrypted by using an appropriate homomorphic cryptosystem. An s.p.e.d. 1-dimensional DCT is obtained by defining a convenient signal model and is extended to the 2-dimensional case by using separable processing of rows and columns. The bounds imposed by the cryptosystem on the size of the DCT and the arithmetic precision are derived, considering both the direct DCT algorithm and its fast version. Particular attention is given to block-based DCT (BDCT), with emphasis on the possibility of lowering the computational burden by parallel application of the s.p.e.d. DCT to different image blocks. The application of the s.p.e.d. 2D-DCT and 2D-BDCT to 8-bit greyscale images is analyzed; whereas a case study demonstrates the feasibility of the s.p.e.d. DCT in a practical scenario.

A robust spread spectrum watermarking method using two levels DCT

In this paper, a discrete cosine transform (DCT) based blind watermarking scheme based on spread spectrum communications is proposed. We perform block-based DCT (BDCT) on the host image; then using the DC coefficients of each block, we construct a low-resolution approximation image. We apply BDCT on this approximation image, then watermark is embedded by adding a pseudo random noise sequence into its high frequencies. In detection stage, we extract the approximation image from the watermarked image, then the same pseudo random noise sequence is generated, and its correlation is computed with high frequencies of the watermarked approximation image. In our method, higher robustness is obtained because of embedding the watermark in low frequencies. In addition, higher imperceptibility is gained by scattering the watermark's bit in different blocks. Compared with related works, our method proved to be highly resistant in cases of many common attacks, while preserving high peak signal to noise ratio for the watermarked images.

저해상도 H.264/AVC 비디오를 위한 개선된 디블럭킹 필터

비디오 압축 표준 중에서 H.264/AVC는 높은 압축률과 신뢰성 있는 비디오 전송을 지원하기 위해 설계된 표준이다. 이는 블록기반의 DCT를 이용하여 압축을 하기 때문에 블럭킹 효과가 나타나며 이를 감소시키기 위한 디블러킹 필터를 사용하지만 영상이 과도하게 평활화 됨으로 화질이 저하된다. 본 논문에서는 이러한 단점을 해결하기 위해 개선된 디블록킹 필터를 제안한다. 제안된 디블록킹 필터는 블록 경계 강도를 재결정하고 저주파 영역에서의 튀는 점(artifacts)을 제거하기 위해 코너 필터링을 적용한다. 성능을 평가하기 위해 제안된 디블록킹 필터와 기존의 방법을 다양한 비디오에 적용하여 주관적인 화질 평가와 객관적인 화질 평가를 하였다. 실험결과는 제안방법은 영상의 에지가 보존되면서 블록킹 현상이 감소되고 PSNR이 개선됨을 보여준다. H.264/AVC among moving picture compression standard is the standard format for high compression rate and reliable video transimission. It generates blocking effects in video due to compressing video using block-based DCT and includes de-blocking filter to reduce blocking effect. Therefore, the filter makes the video over-smoothing and the quality of it is reduced. In this paper, we propose a improved de-blocking filter to solve the demerit. The proposed de-blocking filter redetermine the block boundary strength and apply the comer filtering to eliminate artifacts in low frequency domain. To evaluate the performance, we apply the proposed deblocking filter and exiting method to various video and evaluated the quality of image subjectively and objectively by analyzing the result. The simulation result shows the proposed method preserves the edge of video, reduces blocking effects and improves PSNR than the existing method.

No-reference noticeable blockiness estimation in images

Blocking artifact is a prevailing degradation caused by the block-based Discrete Cosine Transform (BDCT) coding technique under low bit-rate conditions. In this paper, we propose an efficient no-reference noticeable blockiness estimation algorithm for BDCT coded images. The difference on block boundaries is first measured and then transformed into the block discontinuity map. We consider the effects of luminance adaptation and texture masking on blocking and integrate them using a nonlinear operator to form an overall masking map. This map is finally incorporated with the discontinuity map to generate Noticeable Blockiness Map (NBM), which can be used to guide perceptual quality assessment, codec parameter optimization, post-processing, etc. We have demonstrated the validity of the NBM through its applications in no-reference image quality assessment and image deblocking.