Predictive coding techniques are attractive for image codecs because they can yield high compression efficiency while spending few computational resources. In remote sensing, predictive techniques are employed in prom...
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Predictive coding techniques are attractive for image codecs because they can yield high compression efficiency while spending few computational resources. In remote sensing, predictive techniques are employed in prominent standards to transmit images captured by Earth Observation (EO) satellites. Although EO satellites have full duplex capacity, compression standards for spatial data are devised to use the downlink only. Recently, we presented a dual-link image coding system that employs both the uplink and the downlink to accelerate the transmission of such images. The proposed system was introduced in the wavelet-based JPEG2000 standard, which is not well-suited for satellites due to its complexity. This letter approaches the dual-link scheme to a more suitable standard for spatial data based on predictive coding, more precisely, the Lossless Multispectral and Hyperspectral image compression standard CCSDS-123.0-B.2. The proposed method adapts the dual-link image coding scheme to CCSDS-123.0-B-2 by incorporating a quantizer, a lightweight arithmetic coder, and a rate control technique. Experimental results suggest that the resulting system achieves higher coding ratios than CCSDS-123.0-B-2 and JPEG2000 with dual link.
image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information redundancy while empowering the compress...
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Compression for machines is an emerging field, where inputs are encoded while optimizing the performance of downstream automated analysis. In scalable coding for humans and machines, the compressed representation used...
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As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision infere...
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As an increasing amount of image and video content will be analyzed by machines, there is demand for a new codec paradigm that is capable of compressing visual input primarily for the purpose of computer vision inference, while secondarily supporting input reconstruction. In this work, we propose a learned compression architecture that can be used to build such a codec. We introduce a novel variational formulation that explicitly takes feature data relevant to the desired inference task as input at the encoder side. As such, our learned scalable image codec encodes and transmits two disentangled latent representations for object detection and input reconstruction. We note that compared to relevant benchmarks, our proposed scheme yields a more compact latent representation that is specialized for the inference task. Our experiments show that our proposed system achieves a bit rate savings of 40.6% on the primary object detection task compared to the current state-of-the-art, albeit with some degradation in performance for the secondary input reconstruction task.
We propose to employ a saliency-driven hierarchical neural image compression network for a machine-to-machine communication scenario following the compress-then-analyze paradigm. By that, different areas of the image ...
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We propose to employ a saliency-driven hierarchical neural image compression network for a machine-to-machine communication scenario following the compress-then-analyze paradigm. By that, different areas of the image are coded at different qualities depending on whether salient objects are located in the corresponding area. Areas without saliency are transmitted in latent spaces of lower spatial resolution in order to reduce the bitrate. The saliency information is explicitly derived from the detections of an object detection network. Furthermore, we propose to add saliency information to the training process in order to further specialize the different latent spaces. All in all, our hierarchical model with all proposed optimizations achieves 77.1 % bitrate savings over the latest video coding standard VVC on the Cityscapes dataset and with Mask R-CNN as analysis network at the decoder side. Thereby, it also outperforms traditional, non-hierarchical compression networks.
Optimized for pixel fidelity metrics, images compressed by existing image codec are facing systematic challenges when used for visual analysis tasks, especially under low-bitrate coding. This paper proposes a visual a...
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Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people's varying watching situations as well as people's demand for real-time im...
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Multiple description coding (MDC) provides a favorable solution for human-centered image communication, which takes into account people's varying watching situations as well as people's demand for real-time image display. As an effective technique for MDC, three-description lattice vector quantization (3D-LVQ) is considered for image coding in this paper. Based on intra- and inter-correlation in the 3D-LVQ index assignment as well as wavelet intra-subband correlation, a novel predictive decoding method for 3D-LVQ-based image coding is proposed to enhance side decoding performance, which attempts to predict lost descriptions (sublattice points) in a good way for better reconstructions of wavelet vectors (fine lattice points) in the side decoding. Experimental results validate effectiveness of the proposed decoding scheme in terms of rate-distortion performance.
Compressive sensing (CS)-based image coding scheme has been enthusiastically studied, but it still has a poor rate-distortion performance compared with the traditional image coding techniques. In this paper, we propos...
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Compressive sensing (CS)-based image coding scheme has been enthusiastically studied, but it still has a poor rate-distortion performance compared with the traditional image coding techniques. In this paper, we propose a CS multi-layer residual coding scheme to rectify this problem to a certain extent. By dividing CS measurements into multi-layers and predicting a particular layer's measurements with all its preceding layers' measurements, we can transform CS measurements into multi-layer residual coefficients, which are easier to compress. By calculating the residual between the quantized ground-truth CS measurements and their corresponding quantized inference measurements and using Huffman coding to associate each residual quantization index with a binary code, we can reduce the redundancies among CS measurements efficiently. Besides, the prediction and quantization process is designed to be layer-independent, which can save much of the encoding time. The proposed approach introduces a novel framework for using CS in the compression domain. The experimental results show that the proposed scheme can significantly outperform JPEG2000 and approach or reach the performance of HEVC-Intra on some test images.
This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec...
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This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec to encode the prediction errors. First, a novel deep learning-based predictor is proposed to estimate the residuals produced by traditional prediction methods. It is shown that the use of a deep-learning paradigm substantially boosts the prediction accuracy compared with the traditional prediction methods. Second, the prediction error is modeled by a context modeling method and encoded using a novel context-tree-based bit-plane codec. Codec profiles performing either one or two coding passes are proposed, trading off complexity for compression performance. The experimental evaluation is carried out on three different types of data: photographic images, lenslet images, and video sequences. The experimental results show that the proposed lossless coding approach systematically and substantially outperforms the state-of-the-art methods for each type of data.
Transform coding to sparsify signal representations remains crucial in an image compression pipeline. While the Karhunen-Loève transform (KLT) computed from an empirical covariance matrix C¯ is theoretically...
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