scholarly journals Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 66903-66913 ◽  
Author(s):  
Lina Sha ◽  
Wei Wu ◽  
Bingbing Li
Keyword(s):  
Rate Distortion ◽  
Compression Algorithm ◽  
Reference Image ◽  
Image Set ◽  
Set Compression ◽  
Multiple Reference

Rate-Distortion Optimized Sparse Coding With Ordered Dictionary for Image Set Compression

2018 ◽  
Vol 28 (12) ◽  
pp. 3387-3397 ◽  
Author(s):  
Xinfeng Zhang ◽  
Weisi Lin ◽  
Yabin Zhang ◽  
Shiqi Wang ◽  
Siwei Ma ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Rate Distortion ◽  
Image Set ◽  
Set Compression

Lossless Image Set Compression Using Animated FLIF

2021 ◽  
Author(s):  
Takuya Sakurai ◽  
Ushio Inoue
Keyword(s):  
Image Set ◽  
Set Compression

The Research Based on H.264 Embedded Video Systems of Optimized Compression Algorithm

2014 ◽  
Vol 721 ◽  
pp. 788-791
Author(s):  
Yi Liu ◽  
Zhi Jun Cen ◽  
Jia Liu
Keyword(s):  
Video Compression ◽  
Compression Algorithm ◽  
Practical Significance ◽  
Inter Prediction ◽  
Basic Principles ◽  
Feasible Method ◽  
Optimization Function ◽  
Video Systems ◽  
Frame Motion ◽  
Multiple Reference

Analyzes the basic principles of H.264 video compression algorithm, such as integer DCT, multiple reference frame motion estimation, intra prediction, inter prediction; describes the characteristics of embedded systems, propose a feasible method of video signal optimization, by comparing with the experimental results, the influence of the H.264 coding bit rate bottleneck optimization function obtains a good result. These methods and ideas have practical significance, and it provides a strong reference for other development of video systems.

scholarly journals Enhanced Intra Prediction Based on Adaptive Coding Order and Multiple Reference Sets in HEVC

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 703
Author(s):  
Jin Young Lee
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Prediction Method ◽  
Rate Distortion ◽  
Spatial Prediction ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Adaptive Coding ◽  
Advanced Video Coding ◽  
Multiple Reference

High Efficiency Video Coding (HEVC) is the most recent video coding standard. It can achieve a significantly higher coding performance than previous video coding standards, such as MPEG-2, MPEG-4, and H.264/AVC (Advanced Video Coding). In particular, to obtain high coding efficiency in intra frames, HEVC investigates various directional spatial prediction modes and then selects the best prediction mode based on rate-distortion optimization. For further improvement of coding performance, this paper proposes an enhanced intra prediction method based on adaptive coding order and multiple reference sets. The adaptive coding order determines the best coding order for each block, and the multiple reference sets enable the block to be predicted from various reference samples. Experimental results demonstrate that the proposed method achieves better intra coding performance than the conventional method.

scholarly journals A coarse-to-fine scheme for groupwise registration of multisensor images

2016 ◽  
Vol 13 (6) ◽  
pp. 172988141667330 ◽  
Author(s):  
Yinghao Li ◽  
Zhongshi He ◽  
Hao Zhu ◽  
Dongsheng Zou ◽  
Weiwei Zhang
Keyword(s):  
Mixture Model ◽  
Image Data ◽  
Gaussian Mixture ◽  
Reference Image ◽  
Data Sets ◽  
Coarse Registration ◽  
Image Set ◽  
Groupwise Registration ◽  
Improved Performance ◽  
Coarse To Fine

Ensemble registration is concerned with a group of images that need to be registered simultaneously. It is challenging but important for many image analysis tasks such as vehicle detection and medical image fusion. To solve this problem effectively, a novel coarse-to-fine scheme for groupwise image registration is proposed. First, in the coarse registration step, unregistered images are divided into reference image set and float image set. The images of the two sets are registered based on segmented region matching. The coarse registration results are used as an initial solution for the next step. Then, in the fine registration step, a Gaussian mixture model with a local template is used to model the joint intensity of coarse-registered images. Meanwhile, a minimum message length criterion-based method is employed to determine the unknown number of mixing components. Based on this mixture model, a maximum likelihood framework is used to register a group of images. To evaluate the performance of the proposed approach, some representative groupwise registration approaches are compared on different image data sets. The experimental results show that the proposed approach has improved performance compared to conventional approaches.

Image set compression for similar images with priorities

2019 ◽  
Vol 55 (5) ◽  
pp. 262-264
Author(s):  
Lina Sha ◽  
Wei Wu ◽  
Bingbing Li
Keyword(s):  
Similar Images ◽  
Image Set ◽  
Set Compression
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