Bits Per Pixel Research Articles

TWO-STEP PROVIDING OF DESIRED QUALITY IN LOSSY IMAGE COMPRESSION BY SPIHT

Image information technology has become an important perception technology considering the task of providing lossy image compression with the desired quality using certain encoders Recent researches have shown that the use of a two-step method can perform the compression in a very simple manner and with reduced compression time under the premise of providing a desired visual quality accuracy. However, different encoders have different compression algorithms. These issues involve providing the accuracy of the desired quality. This paper considers the application of the two-step method in an encoder based on a discrete wavelet transform (DWT). In the experiment, bits per pixel (BPP) is used as the control parameter to vary and predict the compressed image quality, and three visual quality evaluation metrics (PSNR, PSNR-HVS, PSNR-HVS-M) are analyzed. In special cases, the two-step method is allowed to be modified. This modification relates to the cases when images subject to lossy compression are either too simple or too complex and linear approximation of dependences is no more valid. Experimental data prove that, compared with the single-step method, after performing the two-step compression method, the mean square error of differences between desired and provided values drops by an order of magnitude. For PSNR-HVS-M, the error of the two-step method does not exceed 3.6 dB. The experiment has been conducted for Set Partitioning in Hierarchical Trees (SPIHT), a typical image encoder based on DWT, but it can be expected that the proposed method applies to other DWT-based image compression techniques. The results show that the application range of the two-step lossy compression method has been expanded. It is not only suitable for encoders based on discrete cosine transform (DCT) but also works well for DWT-based encoders.

Correlative Analysis of EZW and SPIHT Compression Algorithms using Sevenlets Wavelet Technique

The research is carried out to find wavelets in image processing of CT(computerized Tomography) JPEG(Joint Photographic Experts Group) medical image for a Lossy Compression. The EZW(Embedded Zerotree Wavelet) and SPIHT(Set Partitioning Hierarchical Trees) algorithms method is implemented to identify the quality of image by DWT(Discrete Wavelet Transform). Quality analysis is processed based on parameters measure such as CR(Compression Ratio), BPP(Bits Per Pixel), PSNR( Peak Signal to Noise Ratio) and MSE(Mean Square Error). Comparison is made to justify having a good image retaining for seven wavelets, how they correlation each other. Using seven wavelets as assigned a new term Sevenlets in this research work. Medical images are very significant to retain exact image with minimizing loss of information at retrieving. The algorithms EZW and SPIHT give better support to wavelets for compression analysis, can be used to diagnosis analysis to have better perception of image corrective measure.

Volumetric medical image compression using inter‐slice correlation switched prediction approach

AbstractWith the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter‐slice information to achieve compression. This article proposes an inter‐slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter‐slice and intra‐slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (Tϒ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra‐slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16‐bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state‐of‐the art techniques in terms of bits per pixel (BPP) for 8‐bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG‐2000, CALIC, JPEG‐LS, M‐CALIC, and 3D‐CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M‐CALIC, 3D CALIC, JPEG‐2000, JPEG‐LS and CALIC respectively for 16‐bit depth image datasets. The average value of compression ratio for 8‐bit and 16‐bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.

An Improved Discrete Patch Based Reversible Data Hiding For Encoded Color Images

This paper proposes a system for encrypting images which is performed by patch wise separable reversible data. In the beginning, with the help of encryption key, a content writer prepares the unique uncompressed image. Then, the data-hider abridge encrypted images with least significant using data smacking key therefore to construct a sparse path that can incorporate with some additional data. The encrypted image data consisting of additional data, will aid the receiver to use only one encryption key to recall both additional data and image after decryption. The proposed system enables the receiver, to bring out the data and retrieving the actual content without an error by using both data-hiding key and encryption keys. The key generated is stored and the log is maintained. Data transmission through public communication system is unsecure due to be inaccuracy and improper exploitation by eavesdropper. The specific problem can be resolved by the technique Steganography, where the hidden messages are written in a way where it is only understood by the sender and intend recipient which gives the complete security for the messages. This proposed method includes Advanced Encryption Standard algorithm for encrypting both the image and the data. In order to achieve efficient working of encryption process, Separable Patch wise Reversible Data Hiding (SPRDH) algorithm is used in rescindable manner. The performance are analyzed using available Classical database images with the metrics Bits per Pixel (BPP). Peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE).

Performance of Discrete Meyer wavelet Transform on Encoding Levels using Computed Tomography Lung Image

The image transmits and storage capability could be a broad appliance in compression. Image compression techniques engage the appropriate and successful transforms and encoding methods to attain the aim. In this work, discrete Meyer transform based image compression algorithm is used for decomposing the image. The effects of different Encoder loops are described based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The finest encoding loop level for compression is also found based on the results.

FFT-ETM based distortion less and high payload image steganography

This paper is presenting a novel high capacity based imperceptible and robust image steganography technique for obscured communication. A considerable literature studied on this domain reveals distortion that drastically affects image quality. These techniques obscure covert data in most significant bits or least significant bits of host image via easy or unsystematic replacement. Such schemes are vulnerable to malevolent attacks like sample pair method, chi-square test, and quality of host image especially badly affected by MSB replacement. Furthermore, such schemes are lacking in carrying maximum covert information as the number of host image pixels and covert image pixels has the ratio 8:1. In our proposed scheme robust and imperceptibility feature is injected using insignificant pixel value divergence of host and a high capacity covert image. We have proposed frequency entropy method that compares frequencies of covert image and host image in FFT (Fast Fourier Transform) domain. The eminent rate of frequency ETM (Entropy Threshold Match) leads to good image quality and information carrying capability. Moreover, our proposed technique also encrypts the secret image in frequency domain with multi flipped permutated random key vector that provides robustness. Therefore, experiments exhibit that this scheme has improved signal to noise ratio and BPP (bits per pixel) in contrast to existing schemes.

Multiple Image Compression in Medical Imaging Techniques using Wavelets for Speedy Transmission and Optimal Storage

Multiple image compression using wavelet based methods including Discrete Wavelet Transform (DWT) through sub band coding (SBC) and decoding are reviewed for their comparative study. True color image compression measuring parameters like compression ratio (CR), peak to signal noise ratio (PSNR), mean square error (MSE), bits per pixel (BPP) are computed using MATLAB code for each algorithm employed. Gray scale image like Magnetic Resonance Imaging (MRI) is chosen for wavelet transform to achieve encoding and decoding using multiple wavelet families and resolutions to examine their relative merits and demerits. Our main objective is to establish advantages of multiple compression techniques (compressions using multiresolution) helpful in transmitting bulk of compressed medical images via different gadgets facilitating early detection and diagnosis followed by treatments or referrals to specialists residing in different parts of the world. Contemporary compression techniques based on wavelet transform can serve as revolutionary idea in medical field for the overall benefit of humanity.

Enhanced method of using Contourlet transform for medical image compression

With the aim of improving the compression performance using contourlet transform, singular value decomposition (SVD) of intermediate sub-bands has been experimented. In this way, the size of contourlet transform sub-bands can be efficiently reduced to induce compression. This novel lossy compression technique enhances the compression performance of contourlet transform and produces good quality image even at lower bit rates. In addition to SVD, normalisation and prediction of decomposed sub band coefficients also improve the compression performance. The method was tested using medical magnetic resonance imaging (MRI) and computed tomography (CT) imaging modalities. The statistical results confirm the efficiency of the proposed method in terms of compression ratio (CR), peak signal to noise ratio (PSNR) and bits per pixel (BPP). This method produces good compression with approximately 47 dB PSNR at bit rate as low as 0.1 BPP. This is suggested good for medical image communication and storage applications such as picture archiving communication system (PACS), radiology information system (RIS), etc., and also helps in easy search and retrieval process.

Enhanced method of using contourlet transform for medical image compression

With the aim of improving the compression performance using contourlet transform, singular value decomposition (SVD) of intermediate sub-bands has been experimented. In this way, the size of contourlet transform sub-bands can be efficiently reduced to induce compression. This novel lossy compression technique enhances the compression performance of contourlet transform and produces good quality image even at lower bit rates. In addition to SVD, normalisation and prediction of decomposed sub band coefficients also improve the compression performance. The method was tested using medical magnetic resonance imaging (MRI) and computed tomography (CT) imaging modalities. The statistical results confirm the efficiency of the proposed method in terms of compression ratio (CR), peak signal to noise ratio (PSNR) and bits per pixel (BPP). This method produces good compression with approximately 47 dB PSNR at bit rate as low as 0.1 BPP. This is suggested good for medical image communication and storage applications such as picture archiving communication system (PACS), radiology information system (RIS), etc., and also helps in easy search and retrieval process.

Selection of Vanishing moments of Symlet and Coiflet Wavelets on CT Images

The main aim of the work is to develop image compression algorithms with high quality and compression ratio. The objective also includes finding out an best algorithm for medical image compression techniques. The objective is also alert towards the choice of the developed image compression algorithm, which do not modify the characterization behavior of the image. In this paper, image compression algorithm based on discrete symlet and coiflet wavelet transform is implemented for decomposing the image. The selection of different vanishing moments are discussed based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum moments for compression are also chosen based on the results.

Data hiding using adaptive LSB and PVD technique resisting PDH and RS analysis

This paper proposes an improved data hiding technique using the principle of least significant bit (LSB) substitution and pixel value differencing (PVD). It addresses two issues: 1) the error block problem (EBP); 2) the fall of boundary problem (FOBP). The image is divided into non-overlapping blocks of two consecutive pixels. The blocks are divided into three levels depending upon the pixel value difference. The level of the block and the pixel difference range decides the hiding capacity of a block. The proposed technique has been compared with related existing techniques in terms of parameters like peak signal to noise ratio (PSNR), quality index (Q), hiding capacity, bits per pixel (BPP), and the count of the blocks suffering from FOBP. The experimental results prove that the proposed technique offers better PSNR and hiding capacity as compared to the related existing techniques. Furthermore, the proposed technique is resistant to pixel difference histogram (PDH) analysis and RS analysis.

Robust secure video steganography using reversible patch-wise code-based embedding

During the process of data hiding for secure transmission, there is possibility of easy accessing of secret data due to availability of various issues in video steganography techniques. The large size of video file is divided into different number of frames in order to process the large amount of data. By using the embedding process, the secret data is easily embedded on the cover video for either large or small amount of information. The process of encryption and decryption is performed to provide high security in data hiding algorithm. In the transformation technique, the effective decryption process arises an issue during the video encryption process. The ineffective restoration of compression process involves in the encryption of input video frames by using Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) transformation. Also, the pixel information are lost due to processing of frame conversion and encoding in transformation technique. Thus, the encryption efficiency is affected by lossy pixel information. Hence, the flexible φ-correction filtering method with Blind Pixel Algorithm (BPA) technique is proposed in this paper to hide secured data without degrading the information. The pixel grouping is performed by replacing the relevant and recurrent pixels with the message information during the encryption process in order to reduce lossy pixel information. Also, the flexible φ-correction filtering method is developed as preprocessing method to remove noise information on cover and secret video by analyzing the pixel value in terms of Fast Fourier Transform (FFT) after performing the pixel optimization with the help of different boundary coefficient value. Thus, the preprocessing and pixel grouping stage in the proposed technique reduces lossy pixel information. Based on the Least Significant Bit (LSB) concept, the pixel value of secret message embeds on pixel value of cover frame sequentially by using the BPA algorithm and the disadvantage of LSB approach is overwhelmed by embedding the set of random pixel value between cover and secret video. The Patch Wise Code Formation (PWCF) algorithm is utilized as video encoding process to provide high security during data hiding process. The BPA algorithm utilizes in reversible manner to achieve efficient working of decryption process. The performance of the proposed Blind-Pixel Based Secure Data Embedding (BPSDA) technique is evaluated for various video sequences in terms of Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Compression Rate (CR), and Bits Per Pixel (BPP).

HIV-1 induced differential expression of cell surface proteins in SupT1 cell lines

The storage facility of image and image transfer is an extensive appliance in image compression. Image compression techniques require the proper and effective transforms and encoding methods to reach the aim. In this work, discrete wavelet transform based image compression algorithm is used for decomposing the image. The effectiveness of different Encoder loops are analyzed based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The optimum encoding loop for compression is also found based on the results.

Deep neural network based single pixel prediction for unified video coding

Classical video prediction methods exploit directly and shallowly the intra-frame, inter-frame and multi-view similarities within the video sequences; the proposed video prediction methods indirectly and intensively transform the frame correlations into nonlinear mappings by using a general deep neural network (DNN) with single output node. Traditional DNN based video prediction algorithms wholly and coarsely forecast the next frame, but the proposed video prediction algorithms severally and precisely anticipate single pixel of future frame in order to achieve high prediction accuracy and low computation cost. First of all, general DNN based prediction algorithms for intra-frame coding, inter-frame coding and multi-view coding are presented respectively. Then, general DNN based prediction algorithm for unified video coding is raised, which relies on the preceding three prediction algorithms. It is evaluated by simulation experiments that the proposed methods hold better performance than state of the art High Efficiency Video Coding (HEVC) in peak signal to noise ratio (PSNR) and bit per pixel (BPP) in the situation of low bitrate transmission. It is also verified by experimental results that the proposed general DNN architecture possesses higher prediction accuracy and lower computation load than those of conventional DNN architectures. It is further testified by experimental results that the proposed methods are very suitable for multi-view videos with small correlations and big disparities.

ZLBM: zero level binary mapping technique for video security

In recent days, providing security to data is a crucial and critical task in many image processing applications. Specifically, video security is an important and demanding concept. For this purpose, some of the embedding, encoding and decoding techniques are mentioned in existing works, but it has some drawbacks such as increased time complexity, computational complexity and memory consumption. Moreover, it does not provide high security during video transmission. To overcome all these issues, a new technique, namely, Zero Level Binary Mapping (ZLBM) is proposed in this paper for video embedding scheme. The motivation of this paper is to provide high security during video transformation by using the video steganography technique. At first, the cover and stego videos are given as the inputs and it will be converted into the video frames for further processing. Here, the Fuzzy Adaptive Median Filtering (FAMF) technique is employed to remove the impulse noise in the video frames. Then, the pixels in the filtered frames are grouped by using the block wise pixel grouping technique. After that, the frames are embedded with the help of ZLBM technique and encoded based on the patch wise code formation technique. On the receiver side, the inverse ZLBM and block wise pixel regrouping techniques are applied to get the original cover and stego videos. The novel concept of this paper is the use of ZLBM and patch wise code formation techniques for video embedding and compression. The main advantages of the proposed system are high security, good quality and reduced complexity. The experimental results evaluate the performance of the proposed video embedding technique in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Compression Ratio (CR), Bits Per Pixel (BPP) and Signal-to-Noise Ratio (SNR).

Medical Image Compression Using Roi and Nroi Images

Advanced medical imaging requires storage space of huge quantities of digitized clinical data. Due to the inhibited bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in spatial domain will be changed into spectral domain after the transformation to attain more compression gains. In our proposed system, separation of the original image in to Region of Interest (ROI) and Non-Region of Interest (NROI) parts is done. Then Lossless compression with Set Partition in Hierarchical Trees (SPIHT) algorithm is used to compress the ROI part and Lossy compression with Haar wavelet transform is used to compress the NON-ROI part. This algorithm gives better Peak Signal Noise Ratio (PSNR)and Bits per pixel (BPP) value for medical images.

An Enhanced Hyper Spectral Image (HSI) Compression Based On Residual Dependent Arithmetic Coder (RDAC)

Background: The Hyper Spectral Image (HSI) compression is a challenging and demanding task in many remote sensing applications, because it has the large hyperspectral data. Optical remote sensing is much increased due to newly imported sensor technologies and advancements. Lossy HSI compression is an essential part for long-terms spectral storage data. In this paper, we provide a new lossy HSI compression algorithm with the help of Residual Dependent Arithmetic Coder (RDAC). Methods: The main intention of this work is to reduce the complexity while compressing the large volume of data by compressing the spectral bands. Here, the Gray Level Co-occurrence Matrix (GLCM) technique is employed to extract the texture features of the given HSI band image. Then, the k-means clustering algorithm is employed to select the reference band in each cluster based on the cluster prominence value. Moreover, the RDAC is used to compress the reference band and the residual band information of each cluster. Finally, the HSI is decompressed with the help of compressed HSI band images. Results: In experiments, the performance of the proposed method is analyzed and evaluated in terms of Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Compression Ratio (CR). Moreover, it is compared with some of the existing HSI compression techniques such as, Set Partitioning in Hierarchical Trees (SPIHT), Joint Photographic Expert Group (JPEG), Set Partitioning Embedded bloCK (3D-SPECK), Inverse Wavelet Transform (IWT) and Reverse Karhunen-Loeve Transform (RKLT). Conclusion: This paper proposes a new RDAC technique for lossy HSI compression. For this purpose, different image processing techniques are used. In this analysis, it is proved that the proposed HSI compression technique provides the best results, when compared to the other techniques. Keywords: 3D-SPIHT and 3D-SPECK, Bits Per Pixel (BPP), Compression Ratio (CR), Hyper Spectral Image (HSI), JPEG 2000, K-Means clustering, Mean Squared Error (MSE), Residual Dependent Arithmetic Coder (RDAC).

Effects of Decomposition Levels of Wavelets in Image Compression Algorithms

Image compression finds extensive application in storage of image and image transfer. Imagecompression techniques require the application of suitable and effective transforms to achieve the task. In this work, Discrete Wavelet Transform based image compression algorithm is used for decomposing the image. The effectiveness of different wavelets with various decomposition levels are analyzed based on the values of Peak signal to Noise Ratio (PSNR), Compression ratio (CR), Means square error (MSE) and bits per pixel (BPP). The optimum compression algorithm is also found based on the results

Efficient Lossless Image Compression using Modified Hierarchical Forecast and Context Adaptive System

Objective: The main intention of this research is to protect the sharpness of image through decreasing the bit rates in compression image. The proposed approach also intends to improve the compression ratio by predicting the diagonal pixels along with the horizontal and vertical pixels of the image. Methods: In this research, Modified Hierarchical Prediction and Context Adaptive (MHPCA) coding is introduced to overcome the issue of enormous prediction error rate near edges and preserves the sharpness of images. MHPCA coding scheme considers the vertical, horizontal and diagonal (left up, left down and right up, right down) predictors to predict pixels of the image. The inclusion of diagonal predictor enhances the prediction accuracy of pixels in hierarchical prediction. Findings: The experimental tests of MHPCA are conducted and the performance is compared with the existing HPCA scheme which proves that the proposed methodology can preserve the sharpness of the image in an efficient manner than HPCA method. The comparison results prove that the proposed methodology is also efficient in terms of performance metrics such as Bits per Pixel (BPP), Compression Ratio, Mean Square Error rate (MSE) and Peak Signal to Noise Ratio (PSNR). Application/Improvement: The findings demonstrate that by using proposed MHPCA scheme the bit rates in compressed images are reduced significantly which preserves the sharpness of the image. MHPCA coding scheme also reduces the error rate considerably and produces higher compression ratio than the existing HPCA scheme. Keywords: Context Adaptive Coding, Lossless Color Image Compression, Modified Hierarchical Prediction

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

ABSTRACTImage compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.