Hadi Amirpour, A. Pinheiro, Manuela Pereira, M. Ghanbari
{"title":"Fast Depth Decision in Light Field Compression","authors":"Hadi Amirpour, A. Pinheiro, Manuela Pereira, M. Ghanbari","doi":"10.1109/DCC.2019.00064","DOIUrl":"https://doi.org/10.1109/DCC.2019.00064","url":null,"abstract":"Pseudo-sequence based light field compression methods are a highly efficient solution to compress light field images. They use state-of-the-art video encoders like HEVC to encode the image views. HEVC exploits Coding Tree Unit (CTU) structure which is flexible and highly efficient but it is computationally demanding. Each CTU is examined in various depths, prediction and transformation modes to find an optimal coding structure. Efficiently predicting depth of the coding units can reduce complexity significantly. In this paper, a new depth decision method is introduced which exploits the minimum and maximum of previously encoded co-located coding units in spatially closer reference images. Minimum and maximum depths of these co-located CTUs are computed for each coding unit and are used to limit the depth of the current coding unit. Experimental results show up to 55% and 85% encoding time reduction with serial and parallel processing respectively, at negligible degradations.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"127 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129866714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fast PU Intra Mode Decision in Intra HEVC Coding","authors":"Kun Duan, Pengyu Liu, Zeqi Feng, Ke-bin Jia","doi":"10.1109/DCC.2019.00082","DOIUrl":"https://doi.org/10.1109/DCC.2019.00082","url":null,"abstract":"As an upgrade for H.264/AVC, high efficiency video coding (HEVC) achieves 50% bitrate reduction under the equivalent visual quality. However, high computational complexity increases dramatically for adopting up to 35 intra prediction modes. To deal with this issue, we explore spatial-temporal correlation between PUs to narrow rough mode decision (RMD) candidate list and rate distortion optimization (RDO) candidate list respectively. A fast PU intra mode decision scheme is proposed for HEVC fast intra encoding. For RMD, the proposed scheme early determines the impossible range of the PU optimal mode in line with spatial statistical analysis theory. It would narrow RMD candidate list and speed up RMD process. First, 4 subsets (Sb1, Sb2, Sb3 and Sb4) are defined in mode set with 33 directional prediction modes as shown in Figure 1. And then, on the basis of spatial statistical analysis theory, the impossible range of the parent PU optimal mode would be judged by its known optimal mode. Last, low probability subsets of current PU are removed, and candidate subsets are determined for current PU. Where ModeParBest and ModeCurBest are the parent PU optimal mode and the current PU optimal mode respectively. For RDO, the proposed scheme tries to combine temporal correlation with intra coding, which adds co-located optimal mode of the previous frame into the RDO list of the current PU. At the same time, the modes in RDO list are decreased focusing on most time-consuming PUs (4 4 PUs, 8 8 PUs) from 8 candidate modes to 3 candidate modes. Experimental results demonstrate that the proposed scheme yields average 29% encoding time reduction with average 1.19% BDBR gain and 0.06dB BDPSNR loss compared with HM16.9. Further, the proposed method implements fast CU encoding without additional computation during the encoding process.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132987500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Space-Efficient Computation of the Burrows-Wheeler Transform","authors":"José Fuentes-Sepúlveda, G. Navarro, Yakov Nekrich","doi":"10.1109/DCC.2019.00021","DOIUrl":"https://doi.org/10.1109/DCC.2019.00021","url":null,"abstract":"The Burrows-Wheeler Transform (BWT) has become an essential tool for compressed text indexing. Computing it efficiently and within little space is essential for the practicality of the indexes that build on it. A recent algorithm (Munro, Navarro & Nekrich, SODA 2017) computes the BWT in O(n) time using O(nlgσ) bits of space for a text of length n over an alphabet of size σ. The result is of theoretical nature and its practicality is far from obvious. In this paper we engineer their solution and show that, while a basic implementation is slow in practice, the algorithm is amenable to parallelization. For a wide range of alphabet sizes, our resulting implementation outperforms all the compact constructions in the space/time tradeoff map. On the smallest alphabets we are outperformed in time, but nevertheless achieve the least space within reasonable time. For example, in DNA sequences, the most widely used application of BWTs, our construction uses 4.84 bits per base and builds the BWT at a rate of 2.13 megabases per second, whereas the closest previous alternative uses around 7.09 bits per base and runs at 4.17 megabases per second.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132030547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of Prediction of Quality Metrics for IR Images for UAV Applications","authors":"Kabir Hossain, Claire Mantel, Søren Forchhammer","doi":"10.1109/DCC.2019.00090","DOIUrl":"https://doi.org/10.1109/DCC.2019.00090","url":null,"abstract":"This study presents a framework to predict, in a No Reference (NR) manner, Full Reference (FR) objective quality metrics. The methods are applied to infrared (IR) images acquired by Unmanned Aerial Vehicle (UAV) and compressed on-board and then streamed to a ground computer. The proposed method computes two kinds of features, namely Bitstream Based (BB) features which are estimated from the H.264 bitstream and Pixel Based (PB) features which are estimated from the decoded images. Two BB features are computed using the H.264 Quantization Parameter (QP) and estimated PSNR [1]. A total of 53 PB features are calculated based on spatial information and the rest of the features are based on NR quality assessment methods [1, 2, 3]. The most relevant ones are selected and nally mapped to predict FR objective scores using Support Vector Regression. For the performance evaluation, the proposed method is trained to predict scores of 6 FR image quality metrics (SSIM, NQM, MSSIM, FSIM, MAD and PSNR-HMA) using a set of 250 IR aerial images compressed at 4 levels with H.264/AVC as I-frames. For the SVR mapping, 80% of the contents are used for training (200 contents or 800 images) and the remaining 200 images (20%) for testing. We have evaluated our model for three cases; all features, only BB features and finally excluding BB features. The average SROCC values obtained are 0.970, 0.962 and 0.943, respectively. The BB only version achieves very close results to that of using all features. Thus the presented NR BB Image Quality Assessment (IQA) method for the considered IR image material is very ecient. We have compared our method with three NR methods [1, 2, 3]. The proposed method is competitive compared to the state-of-the-art NR algorithms.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129695533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kejun Wu, Zongbang Liao, Qiong Liu, Yaguang Yin, You Yang
{"title":"A Global Co-Saliency Guided Bit Allocation for Light Field Image Compression","authors":"Kejun Wu, Zongbang Liao, Qiong Liu, Yaguang Yin, You Yang","doi":"10.1109/DCC.2019.00120","DOIUrl":"https://doi.org/10.1109/DCC.2019.00120","url":null,"abstract":"Light field is the most prospective technology for interactive and immersive visual applications. and light field image is an intermediate data format that demands a large amount of storage space and higher transmission bandwidth. Therefore, compression of light field images is highly desired for further applications. In this paper, we propose a co-saliency guided bit allocation scheme with constraints of consistency among sub-aperture images. Firstly, saliency is jointly detected on color and depth images of sub-aperture by improving our previous model. The obtained pixel-wise co-saliency map is converted into block-wise via K-means clustering. In this way, the saliency weight of each coding tree unit (CTU) can be calculated. Then, target bits of each CTU are initially determined by the weight of each block. The allocation is adjusted dynamically under the guidance of co-saliency map and the image texture complexity. The experimental results show that BD-PSNR of 0.384 dB can be achieved for the salient region at the cost of less than 0.107 dB decrease for the whole image compared to HTM anchor. Moreover, subjective quality of proposed scheme outperforms the anchor for the salient region, and there is no noticeable distortion for non-salient region.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132129591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"BWT Tunnel Planning is Hard But Manageable","authors":"Uwe Baier, K. Dede","doi":"10.1109/DCC.2019.00022","DOIUrl":"https://doi.org/10.1109/DCC.2019.00022","url":null,"abstract":"The Burrows-Wheeler transform is a well known and useful text transformation used for both data compression and text indexing. Recently, a new technique called \"tunneling\" was presented, improving compression rates of BWT compressors by a vast amount. In this paper, we address the problem of \"tunnel planning\", that is, find a good choice of Blocks to be tunneled. We show that, if Blocks are allowed to overlap each other, the corresponding Block cover and maximum coverage problem are NP-hard, while the Block cover problem is in P if no overlappings are allowed. Furthermore, we present a simple heuristic which outperforms existing solutions for Block choice in the overlapping case both in compression rate and resource requirements.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132827063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Event-Triggered Stochastic Control via Constrained Quantization","authors":"Hikmet Yildiz, Yu Su, A. Khina, B. Hassibi","doi":"10.1109/DCC.2019.00124","DOIUrl":"https://doi.org/10.1109/DCC.2019.00124","url":null,"abstract":"We consider a discrete-time linear quadratic Gaussian networked control setting where the (full information) observer and controller are separated by a fixed-rate noiseless channel. We study the event-triggered control setup in which the encoder may choose to either transmit a packet or remain silent. We recast this problem into that of fixed-rate quantization with an extra symbol that corresponds to the silence event. This way, controlling the average transmission rate is possible by constraining the minimal probability of the silence symbol. We supplement our theoretical framework with numerical simulations.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129370021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gayathri Venugopal, Philipp Helle, K. Mueller, D. Marpe, T. Wiegand
{"title":"Hardware-Friendly Intra Region-Based Template Matching for VVC","authors":"Gayathri Venugopal, Philipp Helle, K. Mueller, D. Marpe, T. Wiegand","doi":"10.1109/DCC.2019.00118","DOIUrl":"https://doi.org/10.1109/DCC.2019.00118","url":null,"abstract":"In a template matching (TM) intra method, the neighboring samples of the current block are regarded as a template. The decoder searches for the best template match in the current reconstructed picture using an error minimizing metric like sum of squared differences (SSD). The prediction signal is generated by copying the samples of the adjacent block to the selected template match. The increased decoder complexity from the search algorithm makes it less attractive for modern applications like video calling. In a previous publication [1], we presented a region-based template matching (RTM) approach for intra coding. Compared to the conventional TM methods which searches for the template match in a complete search window, RTM searches in a region of the search window. Thus, RTM offers a better trade-off between coding efficiency and decoder complexity. Nevertheless, the memory requirements and number of computations to be carried at the decoder are still high, making RTM difficult for hardware realization. This paper aims to address these issues.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121716339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Idoia Ochoa, Hongyi Li, Florian Baumgarte, C. Hergenrother, Jan Voges, M. Hernaez
{"title":"AliCo: A New Efficient Representation for SAM Files","authors":"Idoia Ochoa, Hongyi Li, Florian Baumgarte, C. Hergenrother, Jan Voges, M. Hernaez","doi":"10.1109/DCC.2019.00017","DOIUrl":"https://doi.org/10.1109/DCC.2019.00017","url":null,"abstract":"As genome sequencing continues to become more cost-effective and affordable, more raw and aligned genomic files are expected to be generated in future years. In addition, due to the increase in the throughput of sequencing machines, the size of these files is significantly growing. In particular, aligned files (e.g., SAM/BAM) are used for further processing of the data, and hence efficient representation of these files is a pressing need. In this work we present AliCo, a new compression method tailored to the aligned data represented in the SAM format. We demonstrate through simulations on existing datasets that AliCo outperforms in compression ratio, on average, the state-of-the-art compressors for SAM files, achieving more than 85% reduction in size when operating in its lossless mode. AliCo also supports a variety of modes for lossy compression of the quality scores, including for the first time the recently proposed lossy compressor CALQ, which uses information from the aligned reads to adjust the level of quantization for each location of the genome (achieving more than 10× compression gains in high-coverage datasets). AliCo also supports optional compression of the reference sequence used for compression, hence guaranteeing exact reconstruction of the compressed data. Finally, AliCo allows to stream the data as it is being compressed, as well as to decompress the data as it is being received, potentially providing significant time savings. AliCo can be accessed at: https://github.com/iochoa/alico","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"545 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127660592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fast Intra Prediction Algorithm for Virtual Reality 360 Degree Video Based on Improved RMD","authors":"Zhi Liu, Cai Xu, Mengmeng Zhang, Xiaohan Guan","doi":"10.1109/DCC.2019.00105","DOIUrl":"https://doi.org/10.1109/DCC.2019.00105","url":null,"abstract":"Virtual Reality 360 degree video is spherical video. It allows the viewer to freely choose the field of view from any angle, and the video resolution must be high enough for every field of view. During coding process, spherical video is projected into 2D plane video and it has ultra-high resolution and larger data compared to traditional video. Considering reducing the high spatial redundancy of virtual reality 360 degree video, and reducing the computational complexity of intra coding, while ensuring the integrity of the details of each field of view, a fast intra prediction algorithm for virtual reality 360 degree video based on improved Rough Mode Decision (RMD) is proposed. The experimental results show that the proposed algorithm brings 38.73% time saving with only 1.4% Bjontegaard delta rate increase in All-Intra mode.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115493468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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