An Innovative Lossless Compression Method for Discrete-Color Images

alZahir, Saif; Borici, Arber

doi:10.1109/tip.2014.2363411

Cited by 25 publications

(8 citation statements)

References 23 publications

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“…There are 'r' registers in the generator which boost their values at each instant depending on the value of the incoming pointer to the shift register. The main requirements of the pseudo noise code are good spectral characteristics and security [8].…”

Section: Proposed Methodsmentioning

confidence: 99%

An efficient data hiding method in images

Saranya¹

2019

IJECE

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<p class="Abstract">Data Hiding has a huge range of applications in the medical field for transmission. It is helpful in securing the documentation of the patients from the violator with good storage space. The medical images of different modalities like CT, MRI, and PET with the digitized clinical information can be sent to the doctors across the world for the treatment. Due to the bandwidth and storage constraints, medical images along with the clinical information must be compressed before transmission and storage. This paper gives a technique for hiding the digitized clinical information along with the DICOM images in Complex Contourlet Transform (CCT) Domain. It also analyses the compression method by using an Entropy Encoder method. Hence, this work suggests that the data hiding method based on Complex Contourlet Transform (CCT) is efficient and also it has a high hiding capacity. The improved value of Compression Ratio (CR), Space Saving (SS), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) shows that the new method satisfies the properties of the data hiding method.</p>

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Section: Proposed Methodsmentioning

confidence: 99%

An efficient data hiding method in images

Saranya¹

2019

IJECE

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“…In addition to the storage, data are often required to be transmitted over the Internet at the highest possible speed. Due to the constraint in storage facility and limitation in transmission bandwidth, compression of data is vital [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…In [26], the image pixels are predicted by a hierarchical prediction scheme and then the wavelet transform is applied to the prediction error. Some work [5,9,11,[27][28][29][30] applies various types of image transformation or pixel difference or simple entropy coding. An image transformation scheme known as "J bit encoding" (JBE) has been proposed in [11].…”

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confidence: 99%

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Kabir

Mondal

2018

J. Imaging

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Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an "edge-based transformation and entropy coding" (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed "prediction-based transformation and entropy coding" (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: "joint photographic experts group lossless" (JPEG-LS), "set partitioning in hierarchical trees" (SPIHT) and "differential pulse code modulation" (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.compression are some of the wavelet-based compressions such as embedded zerotrees of wavelet transforms (EZW), joint photographic experts group (JPEG) and the moving picture experts group (MPEG) compression.A large number of research papers report image compression algorithms. For example, one study [13] is about discrete cosine transform (DCT)-based lossless image compression where the higher energy coefficients in each block are quantized. Next, an inverse DCT is performed only on the quantized coefficients. The resultant pixel values are in the 2-D spatial domain. The pixel values of two neighboring regions are then subtracted to obtain residual error sequence. The error sequence is encoded by an entropy coder such as Arithmetic or Huffman coding [13]. Image compression in the frequency domain using wavelets is reported in several studies [12,[14][15][16][17]]. In the method described in [14] lifting-based bi-orthogonal wavelet transform is used which produces coefficients that can be rounded without any loss of data. In the work of [18] wavelet transform limits the image energy within fewer coefficients which are encoded by "set partitioning in hierarchical trees" (SPIHT) algorithm.In [19] JPEG lossless (JPEG-LS), a prediction-based lossless scheme, is proposed for continuous tone images. In [14] embedded zero tree coding (EZW) method is proposed based on the zero tree hypothesis. The study in [12] proposes a compression algorithm based on combination of discrete wavelet transform ...

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“…Energyefficient low bit rate image compression in wavelet domain for wireless image sensor networks; in this technique, the authors presented Wavelet transform based approximation band algorithm for low bit rate image compression (Phamil and Amutha, 2015). An innovative lossless compression method for discrete-color images; in this paper relative probability of occurrence of Huffman codes and rowcolumn reduction coding were performed on images (Alzahir and Borici, 2015). A 151 dB High Dynamic Range CMOS Image Sensor Chip Architecture with tone mapping compression embedded in-pixel; in the proposed framework, in-pixel content-aware adaptive global tone mapping algorithm is studied and tone mapping curve on the compression function of illuminations is calculated using histogram (Vargas et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A survey and evaluation of bior wavelet based compression techniques

Hussain

2017

Int. j. adv. appl. sci

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Reconstruction performances of Wavelet for image compressing have been elucidated by various research studies. The purpose of this research is to expound bior Wavelet for the efficient image compression. Wavelet based compression can decompose and reconstruct image into approximate and diagonal details. Mother wavelet refers to a selection of a suitable wavelet function for dilation and translation versions of mother prototype. Many research focus on the selection of most appropriate mother wavelet for image reconstruction. In this research various compression methods investigated on a bior Wavelet. Experimental results show that Adaptively Scanned Wavelet Difference Reduction technique together with bior1.3 has efficient reconstruction capability. Experimental results also show that bior1.3 showed best performance in terms of compression error and compression ratio.

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An Innovative Lossless Compression Method for Discrete-Color Images

Cited by 25 publications

References 23 publications

An efficient data hiding method in images

An efficient data hiding method in images

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

A survey and evaluation of bior wavelet based compression techniques

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