{"title":"Shortest DNA Cyclic Cover in Compressed Space","authors":"Bastien Cazaux, Rodrigo Cánovas, Eric Rivals","doi":"10.1109/DCC.2016.79","DOIUrl":"https://doi.org/10.1109/DCC.2016.79","url":null,"abstract":"For a set of input words, finding a superstring (a string containing each word of the set as a substring) of minimal length is hard. Most approximation algorithms solve the Shortest Cyclic Cover problem before merging the cyclic strings into a linear superstring. A cyclic cover is a set of cyclic strings in which the input words occur as a substring. We investigate a variant of the Shortest Cyclic Cover problem for the case of DNA. Because the two strands that compose DNA have a reverse complementary sequence, and because the sequencing process often overlooks the strand of a read, each read or its reverse complement must occur as a substring in a cyclic cover. We exhibit a linear time algorithm based on graphs for solving the Shortest DNA Cyclic Cover problem and propose compressed data structures for storing the underlying graphs. All results and algorithms can be adapted to the case where strings are simply reversed but not complemented (e.g. in pattern recognition).","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125654481","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":"Lossy Compression of Unordered Rooted Trees","authors":"R. Azaïs, Jean-Baptiste Durand, C. Godin","doi":"10.1109/DCC.2016.73","DOIUrl":"https://doi.org/10.1109/DCC.2016.73","url":null,"abstract":"A classical compression method for trees is to exploit subtree repeats in the structure by representing them by directed acyclic graphs. We propose a lossy compression method that consists in computing a structure with high redundancy that approximates the initial data.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130591575","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}
Rongqun Lin, Yongbing Zhang, Haoqian Wang, Xingzheng Wang, Qionghai Dai
{"title":"Deep Convolutional Neural Network for Decompressed Video Enhancement","authors":"Rongqun Lin, Yongbing Zhang, Haoqian Wang, Xingzheng Wang, Qionghai Dai","doi":"10.1109/DCC.2016.83","DOIUrl":"https://doi.org/10.1109/DCC.2016.83","url":null,"abstract":"Summary form only given: Block-wise intra/inter prediction, transformation and quantization used in block-based hybrid video coding will inevitably result in blocking artifacts, especially at the low bit rate. To address this problem, this paper employs a deep convolutional neural network (CNN) to approximate the reverse function of video compression, motived by the great success of deep learning in computer vision fields recently. The proposed method establishes an end-to-end mapping, represented as the CNN, which takes the decompressed frame as input and outputs the enhanced one. Employing numerous sequences compressed by H.264 and HEVC reference software, the proposed CNN learns the connections between the lossy frame and the original one in an implicit way under different quantization parameters (QP). Figure 1 shows the architecture of our CNN and the pipeline of the network training. We build our network with convolution layers and ReLU layer and the weights and biases of all the convolution layers in our model are updated by minimizing the loss using stochastic gradient descent with the standard backpropagation. We implement the CNN as a post-loop deblocking filter and explore varying CNN parameters for different QPs. Various experimental results demonstrate that the proposed method is able to significantly improve the quality of enhanced frames in terms of both objective and subjective criterions.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123043946","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}
A. J. Díaz-Honrubia, José Luis Martínez, P. Cuenca, H. Kalva
{"title":"A Fast Splitting Algorithm for an H.264/AVC to HEVC Intra Video Transcoder","authors":"A. J. Díaz-Honrubia, José Luis Martínez, P. Cuenca, H. Kalva","doi":"10.1109/DCC.2016.120","DOIUrl":"https://doi.org/10.1109/DCC.2016.120","url":null,"abstract":"The High Efficiency Video Coding (HEVC) standard roughly doubles the rate-distortion compression performance of its predecessor, H.264/AVC, at a cost of a high computational complexity. Moreover, intra sequences are commonly used at video editing or post-production, making it necessary a migration from H.264/AVC to HEVC. This paper aims to propose a fast intra transcoding algorithm from H.264/AVC to HEVC.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124988964","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":"Authorship Attribution Using Relative Compression","authors":"A. Pinho, Diogo Pratas, P. J. Ferreira","doi":"10.1109/DCC.2016.53","DOIUrl":"https://doi.org/10.1109/DCC.2016.53","url":null,"abstract":"Authorship attribution is a classical classification problem. We use it here to illustrate the performance of a compression-based measure that relies on the notion of relative compression. Besides comparing with recent approaches that use multiple discriminant analysis and support vector machines, we compare it with the Normalized Conditional Compression Distance (a direct approximation of the Normalized Information Distance) and the popular Normalized Compression Distance. The Normalized Relative Compression (NRC) attained 100% correct classification in the data set used, showing consistency between the compression ratio and the classification performance, a characteristic not always present in other compression-based measures.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123709330","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}
G. Bjøntegaard, T. Davies, A. Fuldseth, Steinar Midtskogen
{"title":"The Thor Video Codec","authors":"G. Bjøntegaard, T. Davies, A. Fuldseth, Steinar Midtskogen","doi":"10.1109/DCC.2016.74","DOIUrl":"https://doi.org/10.1109/DCC.2016.74","url":null,"abstract":"The Thor video codec is being developed by Cisco with the intention to use only royalty-free coding tools. The video codec is based on the well-known block-based hybrid video coding approach, but with some significant changes compared to existing standards such as AVC/H.264 and HEVC/H.265. These changes include transform coefficient coding, deblocking filter, a separate deringing filter, and interpolated reference frames. Experimental results show 14.5% and 15.3% BDR loss compared to the HEVC/H.265 reference software for low-delay and high-delay configurations respectively. When compared to optimized software implementations of VP9 and x265, Thor provides similar or better compression performance for the same frame rate.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923298","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}
J. Moreno, Beatriz Jaime, A. Rizzi, C. Fernandez-Maloigne
{"title":"Novel Algorithm for Stereoscopic Image Quality Assessment","authors":"J. Moreno, Beatriz Jaime, A. Rizzi, C. Fernandez-Maloigne","doi":"10.1109/DCC.2016.124","DOIUrl":"https://doi.org/10.1109/DCC.2016.124","url":null,"abstract":"Automatic or semi-automatic stereoscopic image quality assessment has arisen due to the recent diffusion of a new generation of stereoscopic technologies and content demand. Thereby, there is a growth in asking for algorithms of Stereoscopic Image Quality Metrics (SIQA). In this paper, we present a method for assessing the stereoscopic image quality, QUALITAS. QUALITAS is grounded on some human visual system features such as contrast sensitivity, effect of disparate image quality in left and right images, and distance perception, which do not depend on the images being tested. QUALITAS is defined in five stages. Instead of averaging individual qualities of the stereo-pair, QUALITAS introduces Contrast Band-Pass Filtering on a wavelet domain at both views, namely our algorithm perceptually weights left and right images depending on certain viewing conditions. This paper includes the comparison of 27 Metrics SIQA proposed by 16 authors, which summarizes the work made in this field in the recent five years, on image database LIVE 3D. Some algorithms can be combined with any 2D/Normal Image Quality Assessments (NIQA), giving as a result that QUALITAS was compared against 221 Metrics. QUALITAS obtained the best results in terms of overall performance of correlation coefficients. We conclude all metrics in SIQA-SET are simple modifications of NIQA, which take into account some extra characteristics from the disparity map (usually depth variances). Instead QUALITAS incorporates disparity masking in addition to divide 3D scenario in two parts: background and foreground planes. Moreover QUALITAS employs a contrast band-pass filtering, so dynamic parameters are considered as observational distance. It includes loss of correlation, luminance and contrast distortion. It takes into account the visual differences between left and right images, employing a penalization depending on their wavelet energy. Thus, the novelty of QUALITAS lies in combining some the best features of stereoscopic image quality assessments.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127621084","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}
Jian Zhang, Chuanmin Jia, N. Zhang, Siwei Ma, Wen Gao
{"title":"Structure-driven Adaptive Non-local Filter for High Efficiency Video Coding (HEVC)","authors":"Jian Zhang, Chuanmin Jia, N. Zhang, Siwei Ma, Wen Gao","doi":"10.1109/DCC.2016.105","DOIUrl":"https://doi.org/10.1109/DCC.2016.105","url":null,"abstract":"Deblocking filter (DF) Is High Efficiency Video Coding (HEVC) is Only Applied to all Samples Adjacent to prediction units (PU), or transform units (TU), which actually exists two issues. The first one is that DF in HEVC does not fully exploit nonlocal similarity structure information in video. The second one is that DF is HEVC does not consider the inside pixels, which often suffer from quantization distrotion. To alleviate these issues, in this paper, a structure-driven adaptive non-local filter (SANF) Is Proposed By Simultaneously Enforcing The Intrinsic Local Sparsity And The Non-Local Self-Similarity Of Each Frame. Not only SANF deals with the boundary pixels, but also the inside area, which is able to effectively reduce block artifacts while enhancing the quality of the deblocked frames. Applying SANF to luma and chroma components after DF, simulation results demonstrate that the proposed SANF can save BD-rate reduction up to 10.3% with ALF off. For luma component, SANF achieves 4.1%. 3.3%, 4.4% BD-rate saving for all intra, low delay B and random access configurations, respectively with ALF off. furthermore, the performance with ALF on is also discussed.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132894747","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":"Content Adaptive Interpolation Filters for HEVC Framework","authors":"Xiaojie Liu, Wenpeng Ding, Yunhui Shi, Baocai Yin","doi":"10.1109/DCC.2016.28","DOIUrl":"https://doi.org/10.1109/DCC.2016.28","url":null,"abstract":"Summary form only given. Motion compensation is the key technique to reduce temporal redundancy in video coding. Interpolation filters are adopted to generate the inter frame prediction for motion compensation with fractional pixel accuracy. In existing video coding standards such as H.264/AVC and HEVC, a set of predefined interpolation filters is adopted in motion compensation. However, predefined interpolation filters cannot adapt to the video content, which may compromise the coding efficiency. In this paper, a content adaptive interpolation scheme is proposed for motion compensation. In the proposed scheme, a set of adaptive interpolation filters is derived for each frame as additional interpolation filters to minimize the inter prediction difference. Rate-distortion optimization is employed to choose between the predefined interpolation filters and the derived adaptive interpolation filters to achieve the best coding performance. The proposed scheme is implemented into the HM 12.1 software and. Experimental results show that the proposed scheme achieves 3.18 percent bit rate saving on average compared with HEVC.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132605636","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":"A Framework of Complexity Optimally Scalable Algorithms for HEVC","authors":"Tingting Wang, Yihao Zhang, Huang Li, Hongyang Chao, Feng Wu","doi":"10.1109/DCC.2016.7","DOIUrl":"https://doi.org/10.1109/DCC.2016.7","url":null,"abstract":"Different from conventional profiles in the state-of-the-art video coding standard HEVC and related optimization methods, we focus on building the optimally scalable algorithms under constrained and varying computational capacity to take full advantages of HEVC as far as possible in order to meet the growing demands of computational capacity adaptive applications such as real-time video communication and video coding on different mobile devices. We propose a video coding framework based on priority order for a special profile and give the general thoughts of designing algorithms by utilizing cost-performance as priority. For the framework, we invent a feasible solution by introducing a three-level coding structure and some novel features that express the relationship between video contents and their priorities. Experimental results partially prove our framework may nearly achieve the optimal coding efficiency under the continuously changeable computing limitations at every time with negligible extra time consuming.","PeriodicalId":302276,"journal":{"name":"2016 Data Compression Conference (DCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131062439","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
