Application-layer forward error correction (FEC) is used in many multimedia communication systems to address the problem of packet loss in lossy packet networks. One powerful form of application-layer FEC is unequal error protection which protects the information symbols according to their importance. We propose a method for unequal error protection with a Fountain code. When the information symbols were partitioned into two protection classes (most important and least important), our method required a smaller transmission bit budget to achieve low bit error rates compared to the two state of the art techniques. We also compared our method to the two state of the art techniques for video unicast and multicast over a lossy network. Simulations for the scalable video coding (SVC) extension of the H.264/AVC standard showed that our method required a smaller transmission bit budget to achieve high quality video.
Unequal loss protection with systematic Reed-Solomon codes allows reliable transmission of embedded multimedia over packet erasure channels. The design of a fast algorithm with low memory requirements for the computation of an unequal loss protection solution is essential in real-time systems. Because the determination of an optimal solution is time-consuming, fast suboptimal solutions have been used. In this paper, we present a fast iterative improvement algorithm with negligible memory requirements. Experimental results for the JPEG2000, 2D, and 3D set partitioning in hierarchical trees (SPIHT) coders showed that our algorithm provided close to optimal peak signal-to-noise ratio (PSNR) performance, while its time complexity was significantly lower than that of all previously proposed algorithms
Abstract-Reliable real-time transmission of packetized embedded multimedia data over noisy channels requires the design of fast error control algorithms. For packet erasure channels, efficient forward error correction is obtained by using systematic Reed-Solomon (RS) codes across packets. For fading channels, state-of-the-art performance is given by a product channel code where each column code is an RS code and each row code is a concatenation of an outer cyclic redundancy check code and an inner rate-compatible punctured convolutional code. For each of these two systems, we propose a low-memory linear-time iterative improvement algorithm to compute an error protection solution. Experimental results for the two-dimensional and three-dimensional set partitioning in hierarchical trees coders showed that our algorithms provide close to optimal average peak signal-to-noise ratio performance, and that their running time is significantly lower than that of all previously proposed solutions.
Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint bit allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the bit allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future.
The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.
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