Recently, two new international image and video coding standards have been released: the wavelet-based JPEG2000 standard designed basically for compressing still images, and H.264/AVC, the newest generic standard for video coding. As part of the JPEG2000 suite, Motion-JPEG2000 extends JPEG2000 to a range of applications originally associated with a pure video coding standard like H.264/AVC. However, currently little is known about the relative performance of Motion-JPEG2000 and H.264/AVC in terms of coding efficiency on their overlapping domain of target applications requiring the random access of individual pictures. In this paper, we report on a comparative study of the rate-distortion performance of Motion-JPEG2000 and H.264/AVC using a representative set of video material. Our experimental coding results indicate that H.264/AVC performs
surprisingly well on individually coded pictures in comparison to the highly sophisticated still image compression technology of JPEG2000. In addition to the rate-distortion analysis, we also provide a brief comparison of the evaluated coding algorithms in terms of complexity and functionality.
There is a considerable amount of literature about image denoising using wavelet-based methods. Some new ideas where also reported using fractal methods. In this paper we propose a hybrid wavelet-fractal denoising method. Using a non-subsampled overcomplete wavelet transform we present the image as a collection of translation invariant copies in different frequency subbands. Within this multiple representation we do a fractal coding which tries to approximate a noise free image. The inverse wavelet transform of the fractal collage leads to the denoised image. Our results are comparable to some of the most efficient known denoising methods.
In this paper, we propose a spatially adaptive wavelet thresholding method using a context model that has been inspired by our prior work on image coding. The proposed context model relies on an estimation of the weighted variance in a local window of scale and space. Appropriately chosen weights are used to model the predominant correlations for a reliable statistical estimation. By iterating the context-based thresholding operation, a more accurate reconstruction can be achieved. Experimental results show that our proposed method yields significantly improved visual quality as well as lower mean squared error compared to the best recently published results in the denoising literature.
The processing of colored documents with Document Management Systems (DMS) is possible with the modern document scanning systems today. Because of the enormous amount of image data generated scanning a typical A4 document with a 300 dpi resolution, image compression is used. The JPEG compression scheme is widely used for such image data. The lack of image quality caused by necessary lossy compression, can significantly reduce the recognition quality of a subsequent optical character recognition (OCR) process, which is essential to any DMS system. LuraDocument, a high performance system for compressing and archiving scanned documents, particularly those containing text and image, is overcoming the gap between high compression and legibility of documents suitable to be managed inside DMS systems. The utilization of LuraDocument results in substantially higher image quality in comparison to standard compression techniques. This high quality is achieved by combining automatic text detection with bitonal compression of text and color/grayscale wavelet compression of images. Since the innovative LuraDocument compression scheme is a complex image processing system, allocating some computational performance, a scalable DSP system has been designed to meet the throughput of high- performance document scanners.
This paper describes in detail the LuraDocument technique, a recently developed, high performance technique for compressing and archiving scanned documents, particularly those containing text and image. LuraDocument offers higher compression rates and quality in comparison to traditional document compression methods, preserving text legibility even at extremely high compression rates. This various stages of LuraDocument compression are described in detail, including image quantization and text detection procedures.