This paper investigates linearly combined motion-compensated signals for video compression. In particular, we discuss multiple motion-compensated signals that are jointly estimated for efficient prediction and video c...
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This paper investigates linearly combined motion-compensated signals for video compression. In particular, we discuss multiple motion-compensated signals that are jointly estimated for efficient prediction and video coding. First, we extend the wide-sense stationary theory of motion-compensated prediction (MCP) for the case of jointly estimated prediction signals. Our theory suggests that the gain by multihypothesis MCP is limited and that two jointly estimated hypotheses provide a major portion of this achievable gain. In addition, the analysis reveals a property of the displacement error of jointly estimated hypotheses. Second, we present a complete multihypothesis codec which is based on the ITU-T Recommendation H.263 with multiframe capability. Multiframe motion compensation chooses one prediction signal from a set of reference frames, whereas multihypothesis prediction chooses more than one for the linear combination. With our scheme, the time delay associated with B-frames is avoided by choosing more than one prediction signal from previously decoded pictures. Experimental results show that multihypothesis prediction improves significantly coding efficiency by utilizing variable block size and multiframe motion compensation. We show that variable block size and multihypothesis prediction provide gains for different scenarios and that multiframe motion compensation enhances the multihypothesis gain. For example, the presented multihypothesis codec with ten reference frames improves coding efficiency by up to 2.7 dB when compared to the reference codec with one reference frame for the set of investigated test sequences.
This paper describes a new type of global, gridded product being created by the Multi-angle Imaging SpectroRadiometer (MISR) team. The product is a compressed version, or summary, of MISR geophysical data products on ...
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This paper describes a new type of global, gridded product being created by the Multi-angle Imaging SpectroRadiometer (MISR) team. The product is a compressed version, or summary, of MISR geophysical data products on a 11 monthly global grid. Data belonging to each grid cell are summarized by a multivariate histogram. The numbers, sizes, and shapes of the histogram bins vary among cells, and they adapt to the shape of the data in high-dimensional space. Also, bin representatives are means rather than midpoints. These modifications allow data to be summarized parsimoniously and with lower error than is possible using customary, simple, descriptive statistics. The method is demonstrated by compressing test MISR aerosol data, and performance is assessed by comparing computations using compressed data with those using the original.
This paper addresses lossy distributed source coding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by ea...
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This paper addresses lossy distributed source coding for acquiring correlated sparse sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements are separately encoded at a finite rate by each sensor, followed by the joint reconstruction of the sources at the decoder. We develop a novel complexity-constrained distributed variable-rate quantized CS method, which minimizes a weighted sum between the mean square error signal reconstruction distortion and the average encoding rate. The encoding complexity of each sensor is restrained by pre-quantizing the encoder input, i.e., the CS measurements, via vectorquantization. Following the entropy-constrained design, each encoder is modeled as a quantizer followed by a lossless entropy encoder, and variable-rate coding is incorporated via rate measures of an entropy bound. For a two-sensor system, necessary optimality conditions are derived, practical training algorithms are proposed, and complexity analysis is provided. Numerical results show that the proposed method achieves superior compression performance as compared with baseline methods, and lends itself to versatile setups with different performance requirements.
In this paper, we propose two fast codebook generation algorithms for entropy-constrained vector quantization. The first algorithm uses the angular constraint to reduce the search area and to accelerate the search pro...
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In this paper, we propose two fast codebook generation algorithms for entropy-constrained vector quantization. The first algorithm uses the angular constraint to reduce the search area and to accelerate the search process in the codebook design. It employs the projection angles of the vectors to a reference line. The second algorithm has feature of using a suitable hyperplane to partition the codebook and image data. These algorithms allow significant acceleration in codebook design process. Experimental results are presented on image block data. These results show that our new algorithms perform better than the previously known methods.
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