Compound document compression with model-based biased reconstruction

The usefulness of electronic document delivery and archives rests in large part on advances in compression technology. Documents can contain complex layouts with different data types, such as text and images, having different statistical characteristics. To achieve better image quality, it is important to make use of such characteristics in compression. We exploit the transform coefficient distributions for text and images. We show that the scheme in base-line JPEG does not lead to minimum mean-square error if we have models of these coefficients. Instead, we discuss an algorithm designed for this performance that involves first classifying the blocks, and then estimating the parameters to enable a biased reconstruction in the decompression value. Simulation results are shown to validate the advantages of this method. © 2004 SPIE and IS&T.published_or_final_versio

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions

Two dimensional (2D) barcodes are becoming a pervasive interface for mobile devices, such as camera phones. Often, only monochrome 2D-barcodes are used due to their robustness in an uncontrolled operating environment of camera phones. Most camera phones capture and store such 2D-barcode images in the baseline JPEG format. As a lossy compression technique, JPEG does introduce a fair amount of error in the decoding of captured 2D-barcode images. In this paper, we introduce an improved JPEG compression scheme for such barcode images. By altering the JPEG compression parameters based on the DCT coefficient distribution of such barcode images, the improved compression scheme produces JPEG images with higher PSNR value as compared to the baseline implementation. We have also applied our improved scheme to a real 2D-barcode system - the QR Code and analyzed its performance against the baseline JPEG scheme

Improving mobile color 2D-barcode JPEG image readability using DCT coefficient distributions

Two dimensional (2D) barcodes are becoming a pervasive interface for mobile devices, such as camera smartphones. Often, only monochrome 2D-barcodes are used due to their robustness in an uncontrolled operating environment of smartphones. Nonetheless, we are seeing an emerging use of color 2D-barcodes for camera smartphones. Most smartphones capture and store such 2D-barcode images in the baseline JPEG format. As a lossy compression technique, JPEG does introduce a fair amount of error in the captured 2D-barcode images. In this paper, we analyzed the Discrete Cosine Transform (DCT) coefficient distributions of generalized 2D-barcodes using colored data cells, each comprising of 4, 8 and 10 colors. Using these DCT distributions, we improved the JPEG compression of such mobile barcode images. By altering the JPEG compression parameters based on the DCT coefficient distribution of the barcode images, our improved compression scheme produces JPEG images with higher PSNR value as compared to the baseline implementation. We have also applied our improved scheme to a 10 colors 2D-barcode system; and analyzed its performance in comparison to the default and alternative JPEG schemes. We have found that our improved scheme does provide a marked improvement for the successful decoding of the 10 colors 2D-barcode system

Efficient high-resolution video compression scheme using background and foreground layers

Video coding using dynamic background frame achieves better compression compared to the traditional techniques by encoding background and foreground separately. This process reduces coding bits for the overall frame significantly; however, encoding background still requires many bits that can be compressed further for achieving better coding efficiency. The cuboid coding framework has been proven to be one of the most effective methods of image compression which exploits homogeneous pixel correlation within a frame and has better alignment with object boundary compared to traditional block-based coding. In a video sequence, the cuboid-based frame partitioning varies with the changes of the foreground. However, since the background remains static for a group of pictures, the cuboid coding exploits better spatial pixel homogeneity. In this work, the impact of cuboid coding on the background frame for high-resolution videos (Ultra-High-Definition (UHD) and 360-degree videos) is investigated using the multilayer framework of SHVC. After the cuboid partitioning, the method of coarse frame generation has been improved with a novel idea by keeping human-visual sensitive information. Unlike the traditional SHVC scheme, in the proposed method, cuboid coded background and the foreground are encoded in separate layers in an implicit manner. Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. Hoang Van. © 2013 IEEE

Efficient compression of motion compensated residuals

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Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems