Significance-linked Connected Component Analysis Research Articles

Embedded Image Compression: A Review

In this paper, the various research papers related to ‘embedded image compression’ are studied. The aim of the present work is to study the methods adopted and analysis and results obtained. The different methods are adopted by various researchers. The present paper includes discrete wavelet transform (DWT), such as embedded zero wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) are studied. Block based discrete cosine transform (DCT) encoders are used in many image and video coding standards. Wavelet-based image coders such as embedded zero tree wavelet (EZW) coder, set partitioning in hierarchical trees (SPIHTs), set partitioning embedded block (SPECK), morphological representations of wavelet data (MRWD) and significance-linked connected component analysis (SLCC) are also the part of embedded image compression. Different types of redundancy present in an image, such as Spatial Redundancy, Statistical Redundancy and Human Vision Redundancy are very necessary for analysis. The JPE -2OOU image compression standard is increasingly gaining widespread importance. Ultra spectral imaging is a relatively recent development which makes quantitative remote sensing of the Earth’s surface possible.

Improving the compression and encryption of images using FPGA-based cryptosystems

Compression and encryption technologies are important to the efficient solving of network bandwidth and security issues. A novel scheme, called the Image Compression Encryption Scheme (ICES), is presented. It combines the Haar Discrete Wavelet Transform (DWT), Significance-Linked Connected Component Analysis (SLCCA), and the Advance Encryption Standard (AES). Because of above reason the ICES efficiently reduce the overall processing time. This study develops a novel hardware system to compress and encrypt an image in real-time using an image compression encryption scheme. The proposed system exploits parallel processing to increase the throughout of the cryptosystem for Internet multimedia applications to implement the ICES. Using hardware acceleration for encryption and decryption, the FPGA implementation of DWT, SLCCA and the AES algorithm can be used. Using a pipeline structure, a very high data throughput of 330 Mbit/s at a clock frequency of 40 MHz was obtained. Therefore, the ICES is secure, fast and suited to high speed network protocols such as ATM (Asynchronous Transfer Mode), FDDI (Fiber Distributed Data Interface) or Internet multimedia applications.

A Progressive Image Compression Method Based on EZW Algorithm

A simple method based on the EZW algorithm is presented for improving image compression performance. Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's EZW(Embedded Zerotree Wavelets)(1), Said and Pearlman's SPIHT(Set Partitioning In Hierarchical Trees)(2), and Bing-Bing Chai's SLCCA(Significance-Linked Connected Component Analysis for Wavelet Image Coding)(3). The EZW algorithm is based on five key concepts: (1) a DWT(Discrete Wavelet Transform) or hierarchical subband decomposition, (2) prediction of the absence of significant information across scales by exploiting self-similarity inherent in images, (3) entropy-coded successive-approximation quantization, (4) universal lossless data compression which is achieved via adaptive arithmetic coding. and (5) DWT coefficients' degeneration from high scale subbands to low scale subbands. In this paper, we have improved the self-similarity statistical characteristic in concept (5) and present a progressive image compression method.

Morphological representation of DCT coefficients for image compression

Recent success in discrete cosine transform (DCT) image coding is mainly attributed to recognition of the importance of data organization and representation. Currently, there are several competitive DCT-based coders such as DCT-based embedded image coding (EZDCT) (see Xiong et al., Z., 1996) and significance tree quantization (STQ) (see Davis, G. and Chawla, S., 1997). In the wavelet context, morphological representation of wavelet data has achieved the best compression performance. The representatives are morphological representation of wavelet data (MRWD) (see Servetto, S. et al., 1999) and significance-linked connected component analysis (see Chai, B.-B. et al., 1999). We show that, by proper reorganization of its coefficients, the block-based DCT can have similar characteristics, such as energy compaction, cross-subband similarity, decay of magnitude across subband, etc., to the wavelet transform. These characteristics can widen DCT applications relevant to image compression, image retrieval, pattern recognition, etc. We then present an image coder utilizing these characteristics by morphological representation of DCT coefficients (MRDCT). The experiments show that MRDCT is among the state-of-the-art DCT-based image coders reported in the literature. For example, for the Lena image at 0.25 bpp, MRDCT outperforms JPEG, STQ and EZDCT in peak signal-to-noise ratio by 1.0, 1.0, and 0.3 dB, respectively.

Scalable, error-resilient, and high-performance video communications in mobile wireless environments

A novel mobile communications system is proposed, which provides not only effective but also efficient video access for mobile users when communicating over low-bandwidth error-prone wireless links. The middleware implemented by a mobile proxy server at the mobile support station is designed for the seamless integration of mobile users with video servers, so the specific details of the underlying protocols and source/channel coding techniques are hidden to both the video server and mobile client. Based on the concept of application-level framing, the application (video codec in our case) plays a significant role in network communications that most of functionalities of the system are implemented as part of the application. As such, at the application layer, adaptive source- and channel-coding techniques are developed to jointly provide the user with the highest possible video quality. For efficient source coding, our high-performance low-complexity video-coding algorithm called a 3-D significance-linked connected component analysis (3D-SLCCA) is chosen. Due to its high robustness against channel-error propagation, 3D-SLCCA is well suited for wireless environments. For error-resilient channel coding, a multilayer transmission error-control mechanism is developed. Since there is no additional requirement imposed on either the mobile client or the video server, mobile users interact with the server in exactly the same way as stationary users. Extensive computer experiments demonstrate the effectiveness of the proposed system.

Significance-linked connected component analysis for wavelet image coding

Recent success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256x256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.

Significance-linked connected component analysis for very low bit-rate wavelet video coding

A novel hybrid wavelet video coding algorithm termed video significance-linked connected component analysis (VSLCCA) is developed for very low bit-rate applications. In the proposed VSLCCA codec, first, fine-tuned motion estimation based on the H.263 Recommendation is developed to reduce temporal redundancy, and exhaustive overlapped block motion compensation is utilized to ensure coherency in motion compensated error frames. Second, the wavelet transform is applied to each coherent motion compensated error frame to attain global energy compaction. Third, significant fields of wavelet-transformed error frames are organized and represented as significance-linked connected components so that both the within-subband clustering and the cross-scale dependency are exploited. Last, the horizontal and vertical components of motion vectors are encoded separately using adaptive arithmetic coding while significant wavelet coefficients are encoded in bit-plane order by using high order Markov source modeling and adaptive arithmetic coding. Experimental results on eight standard MPEG-4 test sequences show that for intraframe coding, on average the proposed codec exceeds H.263 and ZTE (zero-tree entropy) in peak signal-to-noise ratio by as much as 2.07 and 1.38 dB at 28 kbit/s, respectively. For entire sequence coding, VSLCCA is superior to H.263 and ZTE by 0.35 and 0.71 dB on average, respectively.