Multispectral image compression using the wavelet transform

Multispectral images contain information from several spectral wavelengths and currently multispectral images are widely used in remote sensing and they are becoming more common in the field of computer vision and in industrial applications. Typically, one multispectral image in remote sensing may occupy hundreds of megabytes of disk space and several this kind of images may be received from a single measurement. This study considers the compression of multispectral images. The lossy compression is based on the wavelet transform and we compare the suitability of different waveletfilters for the compression. A method for selecting a wavelet filter for the compression and reconstruction of multispectral images is developed. The performance of the multidimensional wavelet transform based compression is compared to other compression methods like PCA, ICA, SPIHT, and DCT/JPEG. The quality of the compression and reconstruction is measured by quantitative measures like signal-to-noise ratio. In addition, we have developed a qualitative measure, which combines the information from the spatial and spectral dimensions of a multispectral image and which also accounts for the visual quality of the bands from the multispectral images.

Anvers, Rudd [non visible sur l'image] gagne [la] finale du 400 m [des Jeux olympiques, seuls apparaissent les coureurs Ainsworth, Shea et Dafel, 20/8/20] : [photographie de presse] / [Agence Rol]

Référence bibliographique : Rol, 60650

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images

Raw as Archival Still Image Format: A Consideration

Source materials like fine art, over-sized, fragile maps, and delicate artifacts have traditionally been digitally converted through the use of controlled lighting and high resolution scanners and camera backs. In addition the capture of items such as general and special collections bound monographs has recently grown both through consortial efforts like the Internet Archive's Open Content Alliance and locally at the individual institution level. These projects, in turn, have introduced increasingly higher resolution consumer-grade digital single lens reflex cameras or "DSLRs" as a significant part of the general cultural heritage digital conversion workflow. Central to the authors' discussion is the fact that both camera backs and DSLRs commonly share the ability to capture native raw file formats. Because these formats include such advantages as access to an image's raw mosaic sensor data within their architecture, many institutions choose raw for initial capture due to its high bit-level and unprocessed nature. However to date these same raw formats, so important to many at the point of capture, have yet to be considered "archival" within most published still imaging standards, if they are considered at all. Throughout many workflows raw files are deleted and thrown away after more traditionally "archival" uncompressed TIFF or JPEG 2000 files have been derived downstream from their raw source formats [1][2]. As a result, the authors examine the nature of raw anew and consider the basic questions, Should raw files be retained? What might their role be? Might they in fact form a new archival format space? Included in the discussion is a survey of assorted raw file types and their attributes. Also addressed are various sustainability issues as they pertain to archival formats with a special emphasis on both raw's positive and negative characteristics as they apply to archival practices. Current common archival workflows versus possible raw-based ones are investigated as well. These comparisons are noted in the context of each approach's differing levels of usable captured image data, various preservation virtues, and the divergent ideas of strictly fixed renditions versus the potential for improved renditions over time. Special attention is given to the DNG raw format through a detailed inspection of a number of its various structural components and the roles that they play in the format's latest specification. Finally an evaluation is drawn of both proprietary raw formats in general and DNG in particular as possible alternative archival formats for still imaging.

Satellite image mosaic of the Terra Nova Bay area (Victoria Land, Antarctica)

This work has been carried out as part of "Programma Nazionale di Ricerche in Antartide" and was supported financially be ENEA through a joint reasearch-program on Antarctic Earth Science with the University of Siena (Italy). The geopmorphological and glaciological research, of which this work forms a part, is coordinated by Prof. Giuseppe Grombelli.

Systematic analysis of the decoding delay in multiview video

We present a framework for the analysis of the decoding delay in multiview video coding (MVC). We show that in real-time applications, an accurate estimation of the decoding delay is essential to achieve a minimum communication latency. As opposed to single-view codecs, the complexity of the multiview prediction structure and the parallel decoding of several views requires a systematic analysis of this decoding delay, which we solve using graph theory and a model of the decoder hardware architecture. Our framework assumes a decoder implementation in general purpose multi-core processors with multi-threading capabilities. For this hardware model, we show that frame processing times depend on the computational load of the decoder and we provide an iterative algorithm to compute jointly frame processing times and decoding delay. Finally, we show that decoding delay analysis can be applied to design decoders with the objective of minimizing the communication latency of the MVC system.

Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O( n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.