Distributed Lossless Coding Techniques for Hyperspectral Images

Abstract

In this paper, we present a novel distributed coding scheme for lossless, progressive and low complexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and low complexity onboard processing are three most prominent ones. To satisfy these requirements, we design a distributed coding scheme that shifts the complexity of data decorrelation to the decoder side to achieve lightweight onboard processing after image acquisition. At the encoder, the images are subsampled in order to facilitate successive encoding and progressive transmission. At the decoder, we generate the side information with adaptive region-based predictor by taking full advantage of the decoded subsampled images and previously decoded neighboring bands based on the assumptions that the objects appearing in different bands are highly correlated. The proposed progressive transmission via subsampling enables the spectral correlation to be refined successively, resulting in gradually improved decoding performance of higher-resolution layers as more sub-images are decoded. Experimental results on the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data demonstrate that the proposed scheme is able to achieve competitive compression performance comparing with the-state-of-the-art 3D schemes, including existing distributed source coding (DSC) schemes. The proposed scheme has even lower encoding complexity than that of the conventional 2D schemes.

Adaptive generalized space shift keying and downlink multi-user transmission in MIMO fading channels

“…2) Network architectures: It's worth emphasizing that our multi-antenna approach applies to other semantic communication systems based on semantic importance, such as Qin's [10] and Dai's [6]. In this section, we have modified our previous MDVSC [7] system developed for image-oriented semantic communication based on semantic importance.…”

“…At the receiver, corresponding inverse transformations are performed. The error-free transmission channel is used to ensure the transmission of the importance matrix W, referring to Qin's design [10]. , then superimposes them with unequal power allocation to obtain Smap super .…”

“…In the research conducted by D. Huang et al, [9], the semantic features were classified into various categories, and the quantization level was adjusted individually for each category. B. Zhang et al [10] designed a universal variable-length semantic and channel coding module that can be utilized in different semantic communication systems. By introducing some proxy functions, the rate allocation scheme is learned in an end-to-end manner.…”

Channel encoding system for transmitting image over wireless network

“…Apart from this there are various other approaches e.g. addressing structurization of block using LDPC [27], turbo coding over multiple-folds [28], LDPC based compressive sensing [29], and lossless encoding over complex image form [30]. Hence, there are multiple variants of encoding approaches attempted over image transmission in existing system.…”

Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression

“…Other methods have also been devised, based on edge detection [14], clustering [15], [16], or vector quantization [17]. Recently, the distributed source coding paradigm has also been used to achieve low-complexity lossless and near-lossless compression [18], [19].…”