Journal Article10.1109/TIP.2020.2984876
A New Polyphase Down-Sampling-Based Multiple Description Image Coding
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TL;DR: The experimental results indicate that the proposed NPDS-MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.
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Abstract: Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each ${N} \times {N}$ image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.
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- 05 Apr 2022
TL;DR: In this paper , a new multiple description coding (MDC) method focusing on interesting objects of the scene, called the region of interest (ROI), is proposed to extract the ROI.
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Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In this article, it was shown that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal $f \in {\cal F}$ decay like a power-law, then it is possible to reconstruct $f$ to within very high accuracy from a small number of random measurements.
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