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The mean-removed vector quantization (MRVQ) scheme achieves good reconstructed image quality, but it requires a high number of bit rates. In this paper, we propose an improved MRVQ scheme. The compressed codes of MRVQ for an image block contain the block mean and the index recording the closest residual vector in the codebook. In the proposed scheme, the block mean values are encoded by the linear prediction technique followed by the Huffman coding technique. The MRVQ indices of the residual vectors are further compressed by the Huffman coding technique. From the experimental results, it is shown that a great deal of bit rate reduction is achieved by using the proposed scheme with acceptable image quality loss.

In this paper, the parallel algorithm of JPEG coding based on GPU is proposed, most image compression systems have efficiency problem and the real-time of wireless multimedia sensor networks (WMSN) which used in image compression and transmission is also an issue need to be solved, so in this paper parallel computation is used in JPEG coding, it is an effective ways to solve these problems. The system of JPEG coding system mainly has eight parts: discrete cosine transform (DCT) and inverse DCT, quantization and inverse quantization, Zig-zag ordering and inverse Zig-zag ordering, Huffman coding and decoding. The proposed parallel algorithm of JPEG coding makes all the parts of JPEG system run on GPU, so the speed of JPEG coding is improved significantly. DCT and Huffman coding are wildly used in image processing; therefore the proposed parallel algorithm can be used in many fields about image compression and processing. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of DCT-JPEG algorithm. The experimental results show that compared with conventional JPEG coding, the maximum speedup ratio of parallel algorithm of JPEG coding can reach more than 120 times, and the reconstructed image has almost the same performance with the serial algorithm in terms of objective quality and subjective effect.

As the digital information must be stored and recovered in an effective ways, it is necessary to compress data in image coding, wavelet transform and lattice vector quantization are adopted in this paper to improve the compression effective of image coding. We first propose the two dimensional spatial wavelet decomposition of image, and then we present lattice Vector Quantization coding scheme for each image subband, lattice vector quantization can make full use of the correlation between the image wavelet coefficients to remove the information redundancy, and the coding method of lattice quantization can be effective because of applying the symmetries of the lattice. The establishment of rate distortion (RD) model suitable for lattice vector quantization of wavelet image coder is also important for image compression, study shows that the RD performance for the spatial subbands are fitted by an exponential form theoretical model, this yields an analytical solution to the bit rate distribution issue, we explore an effective rate control scheme by using the lagrangian optimization method to distribute the bit rate for the spatial subbands. The experimental results show that the proposed algorithm can achieve better compressing effect with minimum loss.

To get better and flexible performance, region-based image coding has been studied widely in the last few years. In region-based image coding, the whole image can be divided into two parts: region-of-interest (ROI) and background area. ROI is coded in low compression ratio, while background area is coded in high compression ratio. In this paper, shape-adaptive DCT (SA-DCT) for coding arbitrarily shaped image segments is introduced. And two region-based image coding methods using discrete cosine transform (DCT) and SA-DCT are presented. The algorithm based on SA-DCT is comparable with the one based on DCT in terms of reconstructed images’ effects. Experimental results show that the algorithm based on SA-DCT performs better than the one based on DCT both in subjective effect and objective effect. Therefore, SA-DCT is a more suitable transform for region-based image coding than DCT.

In this paper, a fast and memory efficient version of set partitioned embedded block (SPECK) image coder is proposed. Due to use of linked lists, SPECK algorithm, requires large run-time memory, making it unsuitable for memory constrained portable multimedia devices and WMSN’s. The proposed coder replace linked list with fixed size static memory, to keep track of set partitions and coding information and use few markers to facilitate coding. Elimination of linked lists also reduces the memory access time and time involved in addition /deletion in memory, thereby making it faster than the original SPECK. Simulation results show that the proposed algorithm outperforms SPECK image coder in terms of memory requirement while retaining the coding efficiency and scalability property of the original SPECK. Proposed coder requires 0.75 bit per pixel state memory which is 9.38 % of the memory required to store the image. Therefore proposed NLSK coder is suitable for resource constrained portable hand held devices and WMSN’s.

The performance of image coding can be improved upon by using a special class of multiplierless discrete cosine transform using Ramanujan numbers termed as Ramanujan DCT (RDCT). Ramanujan ordered numbers are those which approximate 2π/N by 2 2 −l + −m , where l and m are integers. The cosine angles can then be computed using Chebyshev type of recursion using only shifters and adders. Fast forward and backward transformation may be achieved. Analysis and simulations show that the proposed RDCT maintains good de-correlation and energy compaction properties of the DCT and the error due to approximation is almost zero at lower spectral components and relatively low at higher spectral components. Simulation experiments are provided to justify that the proposed algorithm is best suited for image compression.

In this paper, we proposed a progressive time varying source channel coding system for transmitting image over wireless channels. Transmission of compressed image data over noisy channel is an important problem and has been investigated in a variety of scenarios. the core results obtained by a systematic method of instantaneous rate allocation between the progressive source coder and progressive channel coder .It is developed by closed form of expression for end-to-end distortion , rate allocation respectively in static channels. It is extended the static result to an algorithm for fading channels. It is introduced set DCT (blocks) approach is adapted to perform sub band decomposition followed by SPIHT (Set partitioning in Hierarchical tree)

Hyperspectral image classification has received an increasing amount of interest in recent years. However, when representing pixels as vectors, the dimensionality of feature space is high, which causes “curse of dimensionality” problem. In this paper, in order to alleviate the impact of above problem, a manifold sparse coding method is proposed. Firstly, matrix decomposition technique is used to find a concept set and calculates relative data projection in the concept set. Secondly, manifold learning regularization is imported into objective function to capture the intrinsic geometric structure in the data. Finally, LASSO regularization is used to obtain sparse representation of data projection. Experimental results on real hyperspectral image show that the proposed method has better performance than the other state-of-the-art methods.

In this paper, a simple active image authentication scheme for the compressed images of block truncation coding (BTC) is proposed. In this scheme, the authentication codes of the compressed blocks are generated from the random value induced by the random seed. The authentication code of each compressed block is embedded into the bit map. The bit length of each authentication code can be chosen according to the user’s requirement. The experimental results indicate that the proposed scheme detects the tampered areas clearly and keeps good image qualities of the embedded images. Meanwhile, a low computational cost is required in the proposed scheme.

A novel color image compression scheme based on block truncation coding is proposed in this paper. In this scheme, the quadtree segmentation technique is employed to partition the color image into variable-sized image blocks according to its block activities. Then, the block truncation coding scheme with the bit map omission technique is employed to encode the image blocks. According to the experimental results, the proposed scheme significantly cuts down the bit rates while keeping good reconstructed image qualities of the compressed color images.

New innovative research trends are more essential in the area of image compression for various imaging applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm which efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of Peak Signal to Noise Ratio (PSNR). We considered medical images , satellite extracted images and natural images for the inspection and proposed a novel procedure named as Novel Optimized Golomb Rice Coding (NOGR) to increase the visual quality of the reconstructed image. The result of the proposed technique outperforms present techniques and the results are simulated using MATLAB.

New innovative research trends are more essential in the area of image compression for various imaging applications. These applications require good visual quality in processing. In general the tradeoff between compression efficiency and picture quality is the most important parameter to validate the work. The existing algorithms for still image compression were developed by considering the compression efficiency parameter by giving least importance to the visual quality in processing. Hence, we proposed a novel lossless image compression algorithm which efficiently suited for various types of digital images. Thus, in this work, we specifically address the following problem that is to maintain the compression ratio for better visual quality in the reconstruction and considerable gain in the values of Peak Signal to Noise Ratio (PSNR) in two directions of research. We considered medical images , satellite extracted images and natural images for the inspection and proposed a novel procedure named as Novel Optimized Golomb Rice Coding (NOGR) to increase the visual quality of the reconstructed image. The result of the proposed technique outperforms present techniques and the results are simulated using MATLAB.

As an efficient and reliable method in 3D reconstruction and active measurement, structured light vision technology is being more and more important. Structured light vision technology by one or more image coding pattern projection to measure the scenario, and with the position of the projection direction at an angle with the camera intake scene projection image, and then match the projected image and the code pattern corresponding points using trigonometric obtain a 3D scene information. In order to achieve accurate projection image and coding pattern matching, on the one hand need effective structured light coding method, on the other hand, it is necessary to use a variety of image processing method to accurately locate the position of the projection image feature points. In this paper, a color structured light coding and decoding method based on topology structured and regional structured model is proposed. Finally, the methods for extracting encoded feature points based on waveform analysis and for classifying colors based on clustering analysis are developed. The result of the experiment has shown that the proposed method has good performance for the structured light vision system.

디지털 사진․영상․방송 등의 정보에 디지털 신호를 삽입하여 저작권을 주장하는 방안으로 워터마킹 알고리즘을 들 수 있다. 본 논문에서는 QR code와 워터마킹 알고리즘을 적용하는 방법을 제안하였다. 디지털 영상의 DCT 계수의 위치에 따라 QR 코드를 삽입하는 워터마킹 알고리즘을 제안하였다. 본 알고리즘은 디지털영상에 들어 있는 QR코드를 부호화하여 워터마킹 과정을 통하여 각 명암성분의 DCT 영역에 적용하였다. 본 연구를 통하여 워터마킹 과정을 수행한 압축된 영상과 원 영상의 사이의 평균 PSNR값이 33.99dB가 측정되어 화질을 유지함을 알게 되었다. 또한 정규화 상관도 NC가 99.51 이상의 높은 값을 측정하게 되었다

Watermarking is one of the methods that insist on a copyright as it append digital signals in digital informations like still image, video, ets. This paper proposed a novel digital watermarking scheme which combines a QR code and a watermarking method using DCT coefficient. It proposed an algorithm for selecting a QR code from a image and to be converted into a watermark. It processed a watermarking process using expanded watermarking method in DCT area. This simulation results showed that the PSNR difference between the proposed scheme and the original image was 33.98dB and the normalized correlation between the proposed scheme and the original image was above 99.51. The proposed method is reusable.

실시간 부호 매김과 비디오데이터 부호화 변환을 위한 비디오 부호화 방법은 비디오 압축 과정을 가진 통합 부호화 기법을 사용한다. 본 연구에서는 접근이 허용되지 않은 비디오 데이터를 가진 사람이 접근을 하지 못하도록 비디오 데이터 부호화 기법에 대해서 연구 하였다. H.264 엔트로피 코딩에 대한 연구와 H.264 CAVLC 부호화 방법을 이용하여 진보된 비디오 부호화 알고리즘에 대한 연구이다. 특히 보다 더 강한 보안 부호화 프레임을 만들기 위하여 혼합 알고리즘을 제안하였다. 제안한 방법은 비디오 데이터 암호화 기능과 압축률이 호전됨을 실험을 통하여 알 수 있었다.

o achieve real-time encryption and video data transcoding, current video encryption methods usually integrate encryption algorithm with video compression course. This paper is devoted to discussing the video encryption technology, by encrypting to avoid unauthorized person getting video data. This paper studied the H.264 entropy coding and proposed of CAVLC video encryption scheme which is combined with the process of entropy coding of H.264 CAVLC encryption scheme. Three encryption levels are proposed. In addition, a scrambling method is also proposed which makes the encrypted frames more robust in anti crack. This method showed more robust video data encryption function and compressive rate.

A blocked zerotree coding algorithm for compression of subband image is proposed. Significance of blocks with respect to a certain threshold are coded with a set of transition rules for the significance of blocks. Significant blocks are quantized by vector quantization. The basic idea for this coding approach are: 1) Subband images are coded by blocks, 2) Important blocks based on the significance of blocks are coded and quantized, 3) Multiband codebook which is composed of sub-codebooks dedicated for each threshold and subband level is adapted to produce good reproduction vectors for vector quantization. The compression results are similar to Shapiro's zerotree coding even though ours are obtained without entropy coding of bit streams from blocked zerotree encoder. If an entropy coding is applied to the bitstream, PSNR will be improved.

This paper proposes a color image coding algorithm based on shape-adaptive all phase biorthogonal transform (SA-APBT). This algorithm is implemented through four procedures: color space conversion, image segmentation, shape coding, and texture coding. Region-of-interest (ROI) and background area are obtained by image segmentation. Shape coding uses chain code. The texture coding of the ROI is prior to the background area. SA-APBT and uniform quantization are adopted in texture coding. Compared with the color image coding algorithm based on shape-adaptive discrete cosine transform (SA-DCT) at the same bit rates, experimental results on test color images reveal that the objective quality and subjective effects of the reconstructed images using the proposed algorithm are better, especially at low bit rates. Moreover, the complexity of the proposed algorithm is reduced because of uniform quantization.