Image compression using plane fitting with inter-block prediction

Ameer, Salah; Basir, Otman

doi:10.1016/j.imavis.2008.06.005

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…Its frame is shown in Figure 2. Comparison of ITTI and Improved algorithm ROI segmentation is shown in figure [3].…”

Section: Improved Roi Segmentation Algorithmmentioning

confidence: 99%

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An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection

Shuai¹,

Shen²,

Pan³

2015

JAMP

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In order to protect the ROI (region of interest) characteristics while greatly improving medical imaging compression ratio, we are proposing an algorithm for medical imagining compression that is oriented to ROI-characteristics protection. Firstly, an improved ROI segmentation algorithm is put forward based on the analysis of the ROI segmentation. Then, after the ROI segmented, the ROI edge is extracted and encoded with Freeman chain coding. Finally, the ROI is compressed by lossless compression with shearlet; the ROB (region of background) is compressed by the method of high ratio lossy compression combining with Wavelet and Fractal. Simulation results show that the ROI is segmented precisely. It holds edge integrity and has high quality reconstruction processed by the presented method, helping protect ROI characteristics while greatly improving the compression ratio.

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“…Its frame is shown in Figure 2. Comparison of ITTI and Improved algorithm ROI segmentation is shown in figure [3].…”

Section: Improved Roi Segmentation Algorithmmentioning

confidence: 99%

“…Both [1] and [2] acted on the regular rectangular space domain, but imaging segmentation region was often irregular. So [3] proposed the plane fitting method, which was simple and easy. And [4] aimed at the different importance of ROI and ROB.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection

Shuai¹,

Shen²,

Pan³

2015

JAMP

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show abstract

“…In [15], Image compression using plane fitting with interblock prediction is proposed. Compression is achieved by plane fitting scheme.…”

Section: Compression Based On Curve Fittingmentioning

confidence: 99%

Image Compression with Modified Skipline Encoding and Curve Fitting

Sadanandan¹,

Govindan²

2013

IJCA

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High quality digitized images have always been subject to high correlation: high image quality equals large file size. Image Compression is an important issue in Internet, mobile communication, digital library, digital photography, multimedia, teleconferencing and other applications. Application areas of Image Compression would focus on the problem of optimizing storage space and transmission bandwidth. Here a lossy method for image compression based on skip line encoding and curve fitting is proposed. Proposed approach involves two major processing steps: a lossless modified skip line encoding process to eliminate redundant scan lines in the image, and a lossy curve fitting based encoding for further redundancy elimination. The degree of compression is controlled based on the amount of loss that is affordable for applications making use of Peak Signal to Noise Ratio (PSNR) measure in the decision. The results obtained with the combined, modified skip line encoding and curve fitting approach, are analyzed in terms of compression ratio and PSNR. The approach provides improvements in compression ratio for all the tested images. The results obtained were found to be better than a state-of-the-art method in the literature.

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“…Ameer and Basir implemented a scalable image compression scheme via plane fitting by dividing the image into non-overlapping blocks (typically of 8 × 8 pixels) and presented each block with only three or four quantized coefficients. The block center was chosen as the origin followed by simple quantization and coding schemes to reduce the cost [16]. Sadanandan and Govindan proposed a lossy compression method to eliminate the redundancy in the image using two steps, namely skipline encoding and curve-fitting-based encoding [17].…”

Section: Introductionmentioning

confidence: 99%

“…The main weaknesses in all previously mentioned curve-fitting functions (linear or non-linear) in the related work are the edge quality (fine features) of the image and the blocking effects of the reconstructed image. Some works proposed a post-processing [16,19] or pre-processing process [13] to reduce these imperfections; however, this paper proposes an able curve-fitting function to maintain the fine details of compressed image.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

2019

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Image compression is one of the most interesting fields of image processing that is used to reduce image size. 2D curve-fitting is a method that converts the image data (pixel values) to a set of mathematical equations that are used to represent the image. These equations have a fixed form with a few coefficients estimated from the image which has been divided into several blocks. Since the number of coefficients is lower than the original block pixel size, it can be used as a tool for image compression. In this paper, a new curve-fitting model has been proposed to be derived from the symmetric function (hyperbolic tangent) with only three coefficients. The main disadvantages of previous approaches were the additional errors and degradation of edges of the reconstructed image, as well as the blocking effect. To overcome this deficiency, it is proposed that this symmetric hyperbolic tangent (tanh) function be used instead of the classical 1st-and 2nd-order curve-fitting functions which are asymmetric for reformulating the blocks of the image. Depending on the symmetric property of hyperbolic tangent function, this will reduce the reconstruction error and improve fine details and texture of the reconstructed image. The results of this work have been tested and compared with 1st-order curve-fitting, and standard image compression (JPEG) methods. The main advantages of the proposed approach are: strengthening the edges of the image, removing the blocking effect, improving the Structural SIMilarity (SSIM) index, and increasing the Peak Signal-to-Noise Ratio (PSNR) up to 20 dB. Simulation results show that the proposed method has a significant improvement on the objective and subjective quality of the reconstructed image.

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Image compression using plane fitting with inter-block prediction

Cited by 13 publications

References 34 publications

An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection

An Algorithm for Medical Imagining Compression That Is Oriented to ROI-Characteristics Protection

Image Compression with Modified Skipline Encoding and Curve Fitting

Enhancement of Curve-Fitting Image Compression Using Hyperbolic Function

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