Abstract-To store and transmit the large amount of image data necessary for Image-based Rendering (IBR), efficient coding schemes are required. This paper presents two different approaches which exploit three-dimensional scene geometry for multi-view compression. In texture-based coding, images are converted to view-dependent texture maps for compression. In model-aided predictive coding, scene geometry is used for disparity compensation and occlusion detection between images. While both coding strategies are able to attain compression ratios exceeding 2000:1, individual coding performance is found to depend on the accuracy of the available geometry model. Experiments with real-world as well as synthetic image sets show that texture-based coding is more sensitive to geometry inaccuracies than predictive coding. A rate-distortion theoretical analysis of both schemes supports these findings. For reconstructed approximate geometry models, model-aided predictive coding performs best, while texture-based coding yields superior coding results if scene geometry is exactly known.Index Terms-Geometry coding, image-based rendering (IBR), light field compression, model-based coding, multi-view coding analysis, multi-view compression.

We extend a recently-proposed framework for the rate-distortion optimized transmission of packetized media. The original framework assumed that each packet has a single arrival deadline and that a packet is useless if it arrives after its deadline. In practice, however, packets may be associated with multiple deadlines. Examples include the case of compressed video that uses bi-directional prediction and the case of decoders that can recover from late packet arrivals through the accelerated retroactive decoding of the dependency chain. We extend the original framework to consider multiple deadlines. In our experimental results for the case of the accelerated retroactive decoding of late packets, the multiple-deadline formulation yields up to a 1.5 dB improvement in rate-distortion performance compared to the original, singledeadline formulation. The results indicate, furthermore, that accelerated retroactive decoding offers significant benefit only when coupled with a scheduler that considers multiple deadlines.

e application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next a er you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, B for bias and C for cold start. For architecture, we describe a new ranking neural network, focusing on the process that evolved our existing DNN beyond a fully connected two layer network. On handling positional bias in ranking, we describe a novel approach that led to one of the most signi cant improvements in tackling inventory that the DNN historically found challenging. To solve cold start, we describe our perspective on the problem and changes we made to improve the treatment of new listings on the platform. We hope ranking teams transitioning to deep learning will nd this a practical case study of how to iterate on DNNs.

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We propose a novel approach for light field compression that incorporates disparity compensation into 4-D wavelet coding using disparity-compensated lifting. With this approach, we obtain the benefits of wavelet coding, including compression efficiency and scalability in all dimensions. Additionally, our proposed approach solves the irreversibility limitations of previous wavelet coding approaches. Experimental results show that the compression efficiency of the proposed technique outperforms current state-of-theart wavelet coding techniques by a wide margin.