Compound Image Compression Research Articles

An efficient multi balanced cuckoo search K-means technique for segmentation and compression of compound images

The images comprise not only photographic images but also graphic and text images, they are determined in magazines, brochures and websites. The segmentation and compression of compound images (for instance, computer-generated images, scanned documents and so on) are tough to the procedure.The existing segmentation and compression techniques do not provide a complete comprehensive solution. To solve the problems in existing techniques, here we segmented the compound images via an optimization depended on K-means clustering technique along with AC (Alternate Current) coefficient method for the dynamic segmentation and then compressed individually. The AC coefficient based segmentation method results in detachment of smooth (background) and non-smooth (text, image and overlapping) areas. Further, the non-smooth part is segmented via the optimization depended on K-means clustering technique. Also, the density of segmented objects is headed applying different compression strategies such as the Huffman coder, arithmetic coder, and Jpeg coders. With the being approaches, the entire projected architecture is implemented in MATLAB and the function of the scheme is measured and equated. Our proposed system achieves better compression ratio (21.16), and also improves the performance for image quality index (0.931574), PSNR (Peak Signal to Noise Ratio) (34.91338), RMSE (Root Mean Square Error) (0.931574), SSIM (Structural Similarity) (0.546882), and SDME (Second Derivative-like Measure of Enhancement) (44.91293) than the available CS K-means algorithm.

Fuzzy c-Means Based Adversarial Whale Optimization Algorithm for Compound Image Compression

Fuzzy <i>c</i>-Means Based Adversarial Whale Optimization Algorithm for Compound Image Compression

Sparse Decomposition Technique for Segmentation and Compression of Compound Images

Abstract Compression of compound records and images can be more cumbersome than the original information since they can be a mix of text, picture and graphics. The principle requirement of the compound record or images is the nature of the compressed data. In this paper, diverse procedures are used under block-based classification to distinguish the compound image segments. The segmentation process starts with separation of the entire image into blocks by spare decomposition technique in smooth blocks and non smooth blocks. Gray wolf-optimization based FCM (fuzzy C-means) algorithm is employed to segment background, text, graphics, images and overlap, which are then individually compressed using adaptive Huffman coding, embedded zero wavelet and H.264 coding techniques. Exploratory outcomes demonstrate that the proposed conspire expands compression ratio, enhances image quality and additionally limits computational complexity. The proposed method is implemented on the working platform of MATLAB.

AC coefficient and K‐means cuckoo optimisation algorithm‐based segmentation and compression of compound images

Compound images are containing palletise regions including text or graphics and continuous tone images. The compression of compound images is a challenging function and which is complicated to achieve it without degrading the quality of the images. This document is mainly used to improve the compression ratio and an efficient segmentation method is created to separate the background image, text and graphics from the compound images for to make the compression independently. The segmentation is performed through AC coefficient-based segmentation method resulting in smooth and non-smooth regions. The non-smooth region is again segmented by means of K-means cuckoo optimisation algorithm. In the second phase, the segmented background image, text and graphics were compressed by means of arithmetic coder, Huffman coder and JPEG coder, respectively. This proposed technique is implemented in the working platform of MATLAB and the results were analysed.

Efficient oppositional based optimal Harr wavelet for compound image compression using MHE

Image compression is to reduce the size in bytes of a graphics file without corrupting the nature of the image to an unsatisfactory level. The compound image compression ordinarily in view of three classification strategies that is object-based, layer based along with block based. The corruption of compound image or report compression depends on the capacity and transmission of the files. This proposed model has two methods utilized for compression procedure that is optimal harr wavelets and Merging Huffman Encoding (MHE) process. Discrete Wavelet Transform (DWT)-Harr wavelet coefficient optimization analysis encouraged Oppositional based Grey Wolf Optimization (OGWO). Then the compression strategy module based encryption is done. From the outcomes accomplished the highest Compression Ratio (CR) for a compound image that is 97.56% compared with existing systems and furthermore assessed Peak signal to noise ratio (PSNR), Mean square Error (MSE), and Compression Size (CS) of all testing images.

Improved block based segmentation algorithm for compression of compound images

Compression and transmission of compound images are essential processes in real time applications where there is a necessity that the compression technique should attain high compression ratio, low complexity, high PSNR value and a required level of security. This paper proposes a novel method for block-based compound image compression. The pictorial blocks and text/graphics blocks are separated from the compound image. The pictorial blocks are compressed using discrete Haar wavelet transformation and text/graphics colors are mapped to primary colors using color quantization algorithm and the resulting index values are then compressed using the lossless Huffman coding technique. The compressed image is then encrypted using an Advanced Encryption Standard algorithm which ensures that the transmission is fast and highly secured. Experimental results conclude that the proposed algorithm provides better results like low complexity, high compression ratio and a high PSNR value than most of the other image compression techniques.

IMPROVED BLOCK BASED SEGMENTATION FOR JPEG COMPRESSED DOCUMENT IMAGES

Image Compression is to minimize the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level. The compound image compression normally base d on three classification methods that is object ba sed, layer based and block based. This paper presents a block-based segmentati on. for visually lossless compression of scanned do cuments that contain not only photographic images but also text and graphic image s. In low bit rate applications they suffer with un desirable compression artifacts, especially for document images. Existing methods ca n reduce these artifacts by using post processing m ethods without changing the encoding process. Some of these post processing met hods requires classification of the encoded blocks into different categories.

Compound image compression based on unified LZ and hybrid coding

This study proposes a unified LZ and hybrid coding (ULHC) method for compound image and video compression of visually lossless quality and high compression ratio. The method is macroblock-based for ultra-low coding latency and compatibility with the conventional and most popular hybrid video coding standards such as H.264 and MPEG-2. First, each macroblock is coded by two tools: (i) gzip, a popular lossless LZ coding tool, modified to be macroblock-oriented and to be seamlessly unifiable with a lossy hybrid coding tool; (ii) H.264, an advanced lossy hybrid coding tool. Then rate-distortion optimisation is used to select either the modified gzip or H.264 as the final coding. To seamlessly unify these two coding tools for maximum quality and high compression ratio in ULHC, the modified gzip uses the most recent reconstructed and specially serialised macroblock data as the dictionary. Experimental results show that for images and videos composed of natural or synthesised picture, text and graphics, the proposed method provides higher peak signal-to-noise ratio and better subjective quality than H.264 at the same bitrate, and also achieves much higher compression ratio than gzip without any visual quality loss. In fact, ULHC can achieve partial-lossless and partial-near-lossless coding with a high compression ratio.

United coding method for compound image compression

This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video coding. The basic coding unit (BCU) has a size typically between 16?×?16 pixels to 64?×?64 pixels. All coders in UC are used to code each BCU. Then, the lossless coder that generates minimum bit-rate (R) is chosen as the optimal lossless coder. Finally, the final optimal coder is chosen from the lossy intraframe hybrid coder and the optimal lossless coder using R-D cost based optimization criterion. Moreover, the data coded by one lossless coder can be used as the dictionary of other lossless coders. Experimental results demonstrate that compared with H.264, UC achieves up to 20 dB PSNR improvement and better visual picture quality for compound images with mixed text, graphics and natural picture. Compared with lossless coders such as gzip and PNG, UC can achieve 2---5 times higher compression ratio with just a minor loss and keep partial-lossless picture quality. The partial-lossless nature of UC is indispensable for some typical applications, such as cloud computing and rendering, cloudlet-screen computing and remote desktop, where lossless coding of partial image regions is demanded. On the other hand, the implementation complexity and cost increment of UC is moderate, typically less than 25 % of a traditional hybrid coder such as H.264.

Enhanced Hybrid Compound Image Compression Algorithm Combining Block and Layer-based Segmentation

This paper presents an efficient compound image compression method based on both block and layer– based segmentation techniques, which introduces a new hybrid scheme for segmenting compound images. In this hybrid model the image is first segmented into five different blocks using block based classification again the overlapping block is segmented using layer based segmentation for improving the compression ratio, visual quality.

Effective layer-based segmentation of compound images using morphology

Effective compound image compression algorithms require compound images to be first segmented into regions such as text, pictures and background to minimize the loss of visual quality of text during compression. In this paper, a new compound image segmentation algorithm based on the Mixed Raster Content model (MRC) of multilayer approach is proposed (foreground/mask/background). This algorithm first segments a compound image into different classes. Then each class is transformed to the three-layer MRC model differently according to the property of that class. Finally, the foreground and the background layers are compressed using JPEG 2000. The mask layer is compressed using JBIG2. The proposed morphological-based segmentation algorithm design a binary segmentation mask which partitions a compound image into different layers, such as the background layer and the foreground layer accurately. Experimental results show that it is more robust with respect to the font size, style, colour, orientation, and alignment of text in an uneven background. At similar bit rates, our MRC compression with the morphology-based segmentation achieves a much higher subjective quality and coding efficiency than the state-of-the-art compression algorithms, such as JPEG, JPEG 2000 and H.264/AVC-I.

Performance Evaluation of H.264/AVC Intra Compound Image Compression System

This paper presents a comparison of H.264/AVC (Advanced Video Coding) based algorithms for compound images that contain text and natural images. Considered the problem of compressing compound images with a novel idea of using a single coder for compressing all the content types, we investigate the algorithm proposed by two models ZQ-CIC and D-CIC models. The coders presented in this study are very effective for compound documents because of its intraframe prediction mode. When using these coders, it is possible to improve significantly the quality of text regions, with little to negligible losses to the pictorial regions.

Still Image Compression with Mean-Residual Domain Adaptive Resolution Vector Quantization Technique

Abstract With the glowing importance of an advanced image processing methods for digital image, an efficient compression algorithm is increasingly important. In this paper we present progressive image compression algorithm based on Vector Quantization (VQ). Mean-Residual domain Adaptive Resolution Vector Quantization technique (MAR-VQ) that combines the adaptive resolution method with the mean-residual method has been developed. The adaptive resolution method changes the resolution of image adaptively according to the form of a pixel block texture in order to increase the performance of compound image compression. In addition, the mean-residual method was introduced as one of the additional techniques to increase continuous-tone image quality by VQ. As a result, MAR-VQ can realize much superior compression performance than the worldwide standard JPEG 2000.

Compound image compression for real-time computer screen image transmission

We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios.

Optimizing block-thresholding segmentation for multilayer compression of compound images

Compound document images contain graphic or textual content along with pictures. They are a very common form of documents, found in magazines, brochures, Web sites, etc. We focus our attention on the mixed raster content (MRC) multilayer approach for compound image compression. We study block thresholding as a means to segment an image for MRC. An attempt is made to optimize the block threshold in a rate-distortion sense. Also, a fast algorithm is presented to approximate the optimized method. Extensive results are presented including rate-distortion curves, segmentation masks and reconstructed images, showing the performance of the proposed algorithm.