Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225)最新文献_第4页

Line based reduced memory, wavelet image compression 基于行压缩内存，小波图像压缩

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672177

C. Chrysafis, Antonio Ortega

{"title":"Line based reduced memory, wavelet image compression","authors":"C. Chrysafis, Antonio Ortega","doi":"10.1109/DCC.1998.672177","DOIUrl":"https://doi.org/10.1109/DCC.1998.672177","url":null,"abstract":"In this work we propose a novel algorithm for wavelet based image compression with very low memory requirements. The wavelet transform is performed progressively and we only require that a reduced number of lines from the original image be stored at any given time. The result of the wavelet transform is the same as if we were operating on the whole image, the only difference being that the coefficients of different subbands are generated in an interleaved fashion. We begin encoding the (interleaved) wavelet coefficients as soon as they become available. We classify each new coefficient in one of several classes, each corresponding to a different probability model, with the models being adapted on the fly for each image. Our scheme is fully backward adaptive and it relies only on coefficients that have already been transmitted. Our experiments demonstrate that our coder is still very competitive with respect to similar state-of-the-art coders. It is noted that schemes based on zero trees or bit plane encoding basically require the whole image to be transformed (or else have to be implemented using tiling). The features of the algorithm make it well suited for a low memory mode coding within the emerging JPEG2000 standard.","PeriodicalId":191890,"journal":{"name":"Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125965267","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}

引用次数: 392

Analysis and comparison of various image downsampling and upsampling methods 各种图像下采样和上采样方法的分析和比较

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672325

Abdou Youssef

{"title":"Analysis and comparison of various image downsampling and upsampling methods","authors":"Abdou Youssef","doi":"10.1109/DCC.1998.672325","DOIUrl":"https://doi.org/10.1109/DCC.1998.672325","url":null,"abstract":"Summary form only given. The goal is to gain a better understanding of the behavior of the image down/upsampling combinations, and find better down/upsampling methods. We examined existing down/upsampling methods and proposed new ones. We formulated a frequency response approach for understanding and evaluating down/upsampling combinations. The approach was validated experimentally by running the methods on various images and computing the signal to noise ratio (SNR) between the original and the down-then-upsampled images. The frequency response based evaluation correlates well with the experimental evaluation. Down/upsampling combinations were studied in a unified framework. Signals are pre-filtered then decimated by two, resulting in downsampling by two. Afterwards, signals are zero-upsampled by 2, i.e., inserting 0s between successive samples, and then post-filtering. Our analysis showed that for optimal performance, the pre-filter and the post-filter should both be low-pass filters with cutoff at /spl pi//2. We considered five classes of filters. The first corresponds to the simplest down/upsampling combination, decimation/duplication, where decimation is simply the skipping of every other row and every other column, and duplication (for upsampling) involves duplicating every row and every column. The second class corresponds to bilinear interpolation, for both upsampling and downsampling. The third class comprises the biorthogonal and orthogonal wavelets. The fourth class we termed binomial filters. The fifth class consists of least-square FIR filters.","PeriodicalId":191890,"journal":{"name":"Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124071149","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}

引用次数: 8

A Bayesian framework for content-based indexing and retrieval 基于内容的索引和检索的贝叶斯框架

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672322

N. Vasconcelos, A. Lippman

引用次数: 30

Japanese text compression using word-based coding 使用基于单词的编码进行日文文本压缩

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672306

T. Morihara, N. Satoh, H. Yahagi, S. Yoshida

引用次数: 2

Switching between two universal source coding algorithms 在两种通用源编码算法之间切换

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672217

P. Volf, F. Willems

引用次数: 70

Video coding using vector zerotrees and adaptive vector quantization 使用矢量零树和自适应矢量量化的视频编码

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672275

J. Fowler

引用次数: 0

Packed-TS transform [for image compression] 压缩- ts变换[用于图像压缩]

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672327

Bo Zhang, Yuan F. Zheng

{"title":"Packed-TS transform [for image compression]","authors":"Bo Zhang, Yuan F. Zheng","doi":"10.1109/DCC.1998.672327","DOIUrl":"https://doi.org/10.1109/DCC.1998.672327","url":null,"abstract":"Summary form only given. As a reversible integer wavelet transform, the TS transform gains much attention in both lossless and lossy compression. It is a good approximation to the (2, 6) wavelet, which is one of the best biorthogonal wavelets for image compression, and it can be implemented by only using integer addition/subtraction and shift. Most gray-scale images are in 8-bit form; the TS transform coefficients of them can be represented by 16-bit words, while in most modern computers, 32-bit arithmetic and 16-bit arithmetic have the same speed. We propose a method to speed up the TS transform for image compression. The new algorithm is called the packed-TS transform. The proposed method packs two adjacent pixels in one double-word; therefore, it can make use of the 32-bit computational capability of modern computers to accomplish two additions/subtractions in one instruction cycle. The packed-TS transform is also a reversible transform. In the packed-TS transform, the two adjacent pixels or coefficients are stored in the high-word and the low-word of a double-word, respectively. Then the decomposition/reconstruction is performed on this double-word. We compare the performance of the original TS transform and the proposed packed-TS transform on five images: Girl, Lena, Peppers, Couple and Man, respectively . The experiment shows that the packed-TS transform is faster than the original TS transform by about 30 percent, with a comparable performance in the quality of the reconstructed images.","PeriodicalId":191890,"journal":{"name":"Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129302406","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}

引用次数: 0

Compression of unicode files unicode文件的压缩

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672274

P. Fenwick, S. Brierley

引用次数: 11

A perceptual preprocessor to segment video for motion estimation 一种用于视频分割运动估计的感知预处理器

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672255

Yi-jen Chiu

{"title":"A perceptual preprocessor to segment video for motion estimation","authors":"Yi-jen Chiu","doi":"10.1109/DCC.1998.672255","DOIUrl":"https://doi.org/10.1109/DCC.1998.672255","url":null,"abstract":"Summary form only given. The objective of motion estimation and motion compensation is to reduce the temporal redundancy between adjacent pictures in a video sequence. Motion estimation is usually performed by calculating an error metric, such as mean absolute error (MAE), for each block in the current frame over a displaced region in the previously reconstructed frame. The motion vector is attained as the displacement having the minimum error metric. Although this achieves minimum-MAE in the residual block, it does not necessarily result in the best perceptual quality since the MAE is not always a good indicator of video quality. In low bit rate video coding, the overhead in sending the motion vectors becomes a significant proportion of the total data rate. The minimum-MAE motion vector may not achieve the minimum joint entropy for coding the residual block and motion vector, and thus may not achieve the best compression efficiency. In this paper, we attack these problems by introducing a perceptual preprocessor which takes advantage of the insensitivity of the human visual system (HVS) to mild changes in pixel intensity in order to segment the video into regions according to the perceptibility of the picture changes. Our preprocessor can exploit the local psycho-perceptual properties of the HVS because it is designed to segment video in the spatio-temporal pixel domain. The associated computational complexity for the segmentation in the spatio-temporal pixel domain is very small. With the information of segmentation, we then determine which macroblocks require motion estimation.","PeriodicalId":191890,"journal":{"name":"Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116299992","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}

引用次数: 1

A fast renormalisation for arithmetic coding 算术编码的快速重整化

Proceedings DCC '98 Data Compression Conference (Cat. No.98TB100225) Pub Date : 1998-03-30 DOI: 10.1109/DCC.1998.672314

M. Schindler

引用次数: 37