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Highly discriminative 3D shape representations can be formed by encoding the spatial relationship among virtual words into the Bag of Words (BoW) method. To achieve this challenging task, several unresolved issues in the encoding procedure must be overcome for 3D shapes, including: 1) arbitrary mesh resolution; 2) irregular vertex topology; 3) orientation ambiguity on the 3D surface; and 4) invariance...
Graph-based models have recently attracted attention for their potential to enhance transform coding image compression thanks to their capability to efficiently represent discontinuities. Graph transform gets closer to the optimal KLT by using weights that represent inter-pixel correlations but the extra cost to provide such weights can overwhelm the gain, especially in the case of natural images...
A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing "original pixel intensity"-based coding approaches using traditional image coders (e.g. JPEG) to the "residual"...
Depth maps are becoming increasingly important in the context of emerging video coding and processing applications. Depth images represent the scene surface and are characterized by areas of smoothly varying grey levels separated by sharp edges at the position of object boundaries. To enable high quality view rendering at the receiver side, preservation of these characteristics is important. Lossless...
On-board data compression is a critical task that has to be carried out with restricted computational resources for remote sensing applications. This paper proposes an improved algorithm for onboard lossless compression of hyperspectral images, which combines low encoding complexity and high-performance. This algorithm is based on hybrid prediction. In the proposed work, the decorrelation stage reinforces...
In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances. Finally, we report experimental results achieved with...
MODIS data is increasingly important for oceanographic, terrestrial, and atmospheric science observation. Because of the high data rate, the loss less data compression becomes vital for MODIS data transmission and storage. In this paper we present a new approach for loss less compression of MODIS data based on the maximum spanning tree and 3D context prediction. First we determine the prediction sequence...
This paper investigates the application of lossy distributed source coding to high resolution multispectral images. The choice of distributed source coding is motivated by the need for very low encoding complexity on space and airborne platforms. The data consists of red, blue, green and infra-red channels and is compressed in an asymmetric Wyner-Ziv setting. One image channel is compressed using...
In this paper, a multiband loss less compression system exploiting inter-band data correlation is presented. We develop an adaptive prediction scheme that can dynamically switch among smooth, intra and intra-band prediction modes subject to the data correlation. Specifically, we use context information to determine the inter-band correlation between neighboring bands, and then borrow the wisdom of...
In this paper, we analyze the correlation between wavelet coefficients. In order to use both inter-band and intra-band correlation, we build context prediction model for significance and refinement bits. Combining with sign bit prediction model of JPEG2000, we propose a lossless image compression algorithm. Compression experimental results show that our algorithm outperforms both JPEG2000 and JPEG-LS...
The conventional embedded wavelet image coder exploiting the adjacent neighbors is limited to efficiently compress the sign coefficients, since the wavelet coefficients are highly correlated along the dominant image features, such as edges or contours. To solve the problem, in this paper, we propose the direction-adaptive sign context modeling by adaptively exploiting the neighbors suitable to the...
Bayer pattern has been used in most color digital image sensors; FELICS is a smart algorithm to realize lossless compression. This paper does research on the possibility of combining both the characteristics of Bayer and FELICS to eliminate 3-color-channels transformation. After studying the context correlation in Bayer pattern by FELICS theory in an adjusted way, the approach of modifying original...
This paper proposes a novel grayscale image compression approach using the binary wavelet transform (BWT) and context-based arithmetic coding, namely the context-based binary wavelet transform coding algorithm (CBWTC). In our CBWTC, in order to alleviate the degradation of predictability caused by the BWT and eliminate the correlation within the same level subbands, three highpass wavelet coefficients...
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