An Improved Image Compression Algorithm Using Binary Space Partition Scheme and Geometric Wavelets

Chopra, Garima; Pal, Abhijit Kumar

doi:10.1109/tip.2010.2056378

Cited by 26 publications

(21 citation statements)

References 20 publications

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“…Geometric wavelet is a recent development in the field of multivariate piecewise polynomial approximation. Here the binary space partition scheme which is a segmentation based technique of image coding is combined with the wavelet technique [51]. The discrete wavelet transforms have the ability to solve the blocking effect introduced by the DCT.…”

Section: V2 Wavelet Based Hybrid Techniquesmentioning

confidence: 99%

Hybrid Approaches to Image Coding: A Review

Rehna¹,

Jeyakumar²

2011

IJACSA

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Abstract-Now a days, the digital world is most focused on storage space and speed. With the growing demand for better bandwidth utilization, efficient image data compression techniques have emerged as an important factor for image data transmission and storage. To date, different approaches to image compression have been developed like the classical predictive coding, popular transform coding and vector quantization. Several second generation coding schemes or the segmentation based schemes are also gaining popularity. Practically efficient compression systems based on hybrid coding which combines the advantages of different traditional methods of image coding have also being developed over the years. In this paper, different hybrid approaches to image compression are discussed. Hybrid coding of images, in this context, deals with combining two or more traditional approaches to enhance the individual methods and achieve better quality reconstructed images with higher compression ratio. Literature on hybrid techniques of image coding over the past years is also reviewed. An attempt is made to highlight the neuro-wavelet approach for enhancing coding efficiency.

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Section: V2 Wavelet Based Hybrid Techniquesmentioning

confidence: 99%

Hybrid Approaches to Image Coding: A Review

Rehna¹,

Jeyakumar²

2011

IJACSA

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“…Most current encryption schemes [1][2][3] didn't consider compressibility of image data after encryption. In fact, many compressibility scheme of color image [4][5][6] cannot be utilized in encrypted images as order of arrangement as well as pixel value will change, and the correlation among pixels is disorganized. Normally the image should be encrypted after compressed [7] .…”

Section: Introductionmentioning

confidence: 99%

Encryption and Compression Scheme of Color Image Based on a Hyperchaotic System

Jiao¹,

Gong²

2013

Int. J. Onl. Eng.

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Abstract-In order to encrypt and compress a color image simultaneously, an encryption and compression algorithm for color image was proposed based on a hyperchaotic system. A hyperchaotic system was utilized to make a pure white image into a uniform distribution and colorful palette. In compression process, indexing technology was utilized to map the 3D data of color image to two-dimensional subscript of palette. The compressed image would continue to be encrypted by loop feedback in physical space and time space. Simulation experiments show that the algorithm is realized to encrypt and compress a color image simultaneously. It has large key space, high security, and big compression ratio as well as great application value in secure communication of color image.

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“…Most successful lossless image compression algorithms are, however, contextbased and they exploit the 2-D spatial redundancy in natural images (Zhang and Adjeroh, 2008). Examples include LJPG (lossless JPEG), Context-based Adaptive Lossless Image Coding (CALIC) (Paul et al, 2011) and SPIHT (Chopra and Pal, 2011). These methods usually involve four basic components: an initial prediction scheme to remove the spatial redundancy between neighboring pixels; a context selection strategy for a given position in the image; a modeling method for the estimation of the conditional probability distribution of the prediction error given the context in which it occurs; and an entropy coding method based on the estimated conditional probabilities.…”

Section: Introductionmentioning

confidence: 99%

Still Image Compression Using Texture and Non Texture Prediction Model

Mohanbaabu¹,

Renuga²

2012

American Journal of Applied Sciences

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Problem statement: Existing lossless image compression schemes attempt to do prediction in an image data using their Local Binary Pattern (LBP) and their spatial neighborhood based techniques. In the previous techniques such as Vector Quantization (VQ) and Gradient Adjusted Prediction (GAP) the texture and non-texture regions are not classified separately. Texture and Nontexture images prophecy has been a key factor in efficient lossless image compression. Hence, there is a need to develop a more efficient image prediction scheme to exploit these texture components. Approach: In this research, an efficient visual quality technique for image compression is proposed. The image is classified into texture and non-texture regions by using an Artificial Neural Network (ANN) Classifier. The texture region is encoded with the Similar Block Matching (SBM) encoder and the non-texture region is encoded with SPIHT encoding. Results: The proposed texture prediction based compression is compared with the existing compression techniques such as H.264 and JPEG. From the result it reveals that the Peak Signal to Noise Ratio (PSNR) values of all the test images is higher in the proposed technique as compared to JPEG technique. Similarly PSNR values are low in H.264 for all the images except Boat image when compared to the proposed technique. This result concludes that the increase in PSNR indicates that the output image has less noise as compared to existing techniques. Conclusion: The compression of the proposed algorithm is superior to JPEG and H.264. Our new method of compression algorithm can be used to improve the performance of Compression ratio and Peak Signal to Noise Ratio (PSNR). In future this study can be extended to real time applications for video compression in medical images.

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An Improved Image Compression Algorithm Using Binary Space Partition Scheme and Geometric Wavelets

Cited by 26 publications

References 20 publications

Hybrid Approaches to Image Coding: A Review

Hybrid Approaches to Image Coding: A Review

Encryption and Compression Scheme of Color Image Based on a Hyperchaotic System

Still Image Compression Using Texture and Non Texture Prediction Model

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