Fiber optic communication while drilling can transmit downhole geological and engineering parameter data at high speed, serving as a crucial component of intelligent drilling. However, the complex and highly dynamic d...
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Fiber optic communication while drilling can transmit downhole geological and engineering parameter data at high speed, serving as a crucial component of intelligent drilling. However, the complex and highly dynamic downhole environment, characterized by strong vibrations and high temperatures, can lead to random attenuation of light intensity, phase mismatch, and signal distortion, thereby reducing the signal-to-noise ratio of the optical signal. This paper proposes a superposition UEP-Spinal code structure to enhance the noise resistance of signals under low SNR conditions. This structure utilizes Unequal Error Protection and adjusts the duty cycle of the superposition weighting factor P to separately encode the head and tail codes of the signal, simplifying the structural complexity of the tail code and reducing complexity by 2.01dB for the same code length. Meanwhile, it enhances the noise resistance of the head code, reducing the bit error rate by 1.21dB for the same channel capacity, and the overall decoding complexity is reduced by 13.3%. The results demonstrate that the superposition UEP-Spinal code can achieve stable and reliable communication in low SNR environments.
Scalable video coding is an important mechanism to provide several types of end-user devices with different versions of the same encoded bitstream. However, scalable video encoding remains a computationally expensive ...
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Scalable video coding is an important mechanism to provide several types of end-user devices with different versions of the same encoded bitstream. However, scalable video encoding remains a computationally expensive operation. To decrease the complexity we propose generic techniques. These techniques are generic in a sense that they can be combined with existing fast mode decision methods and optimizations. We show that extending such an existing fast mode decision technique yields an average complexity reduction of 87.27%, while only an additional 0.74% of bit rate increase and a decrease of 0.11dB in PSNR is required, compared to the original fast mode decision method(1).
In this paper, we present a novel distributed coding scheme for lossless, progressive and lowcomplexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly diffe...
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In this paper, we present a novel distributed coding scheme for lossless, progressive and lowcomplexity compression of hyperspectral images. Hyperspectral images have several unique requirements that are vastly different from consumer images. Among them, lossless compression, progressive transmission, and lowcomplexity 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 encodingcomplexity than that of the conventional 2D schemes.
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