IMAGE COMPRESSION SCHEME WITH EMBEDDED MULTILEVEL BLOCK TRUNCATION CODING BASED ON HUMAN VISUAL EFFECT

A prequantizer with the human visual effect for the DPCM

Vector quantization (VQ) and block truncation coding (BTC) are successful image compression techniques. However, a reproduced image using VQ or BTC suffers from edge degradation. A new technique that combines the advantages of both VQ and BTC to combat this degradation is presented and is referred to as VQ-BTC. In VQ-BTC, a low-detail block is encoded using VQ. For a high-detail block, a modification of BTC is used to determine the locations of the relatively lighter and relatively darker pixels inside the block and VQ is then used to encode each. VQ-BTC provides improved edge reproduction and much lower bit rates than those obtained by BTC. >

Data hiding is a technique that embeds information into digital cover data. This technique has been concentrated on the spatial uncompressed domain, and it is considered more challenging to perform in the compressed domain, i.e., vector quantization, JPEG, and block truncation coding (BTC). In this paper, we propose a new data hiding scheme for BTC-compressed images. In the proposed scheme, a dynamic programming strategy was used to search for the optimal solution of the bijective mapping function for LSB substitution. Then, according to the optimal solution, each mean value embeds three secret bits to obtain high hiding capacity with low distortion. The experimental results indicated that the proposed scheme obtained both higher hiding capacity and hiding efficiency than the other four existing schemes, while ensuring good visual quality of the stego-image. In addition, the proposed scheme achieved a low bit rate as original BTC algorithm.

Now a days there is a fantasy to be digitized rapidly. Where digitization is applicable to every domain; no matter its technical or non-technical. So there is a need of effective retrieval of data. Today people are finding some new retrieval schemes over the traditional query based retrieval systems. So moving towards content based approach is an hour's need. Some betterments are definitely needed to empower the Content Based approach. For content based video retrieval (CBVR) system, Block Truncation Coding (BTC) is one of the techniques to extract color features of a video. One more technique is there with improvements to BTC is Thepade's Sorted Ternary Block Truncation Coding (TSTBTC). Transform feature extraction is one more technique about extraction of the video. Right now only one feature can be targeted in a technique; in other words, there is no method which can be exposed to color as well as transform feature of a video. In other hand, Vector Quantization (VQ) is a popular technique for lossy data compression. If VQ is used with TSTBTC, it can deal with color features as well as transform features of a video. Because VQ is can be operated with hybrid features. This is first time when VQ is seen in CBVR. This paper also describes the efficiency and accuracy of this technique with different Color Spaces.

We describe a post-processing method which is designed to remove the picture impairments typically introduced by spatial domain block coding techniques, such as vector quantization (VQ) and block truncation coding (BTC). In these coding methods, the quantization noise is highly correlated with the characteristics of the original signals, so that different areas of the coded image suffer from distinctly different impairments. In particular, uniform areas suffer from the blocking effect, where the boundaries of adjacent blocks become visible resulting in false contours. Moreover, all diagonal edges appear jagged, which is known as the staircase effect. Finally, most of the textured areas are 'washed-out'. The post-processing method exploits the properties of smoothed image gradient data to distinguish between high and low detail regions of the decoded image. The filtering process is then decided based on the region classification. For the high detail image areas, which include edges and texture, a fully adaptive Gaussian-type filter is employed. The shape and the position of the Gaussian kernel is adapted according to the characteristics of each local region. For the low detail image regions, which include areas of constant or slowly varying intensity, a simple Gaussian smoothing operator is used. Simulation results confirm that, after applying this post-filtering algorithm to the coded images, the annoying blocking effect is reduced and the jagged edges appear continuous and sharp.

In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the communication, the sender first compresses an image and then refines the characteristic values from the compressed image. The characteristic values are encrypted to digital signatures using the RSA scheme (proposed by Rivest, Shamir and Adleman). The signatures are embedded into the compressed image and thus the image integrity can be authenticated after transmission. Upon receipt of the compressed image, the receiver extracts the signatures and then decrypts the characteristic values using the sender's public key. Another characteristic value can also be decoded by the image compression scheme. By comparing a few characteristic values, the system determines whether or not the decompressed image is tampered and then outputs a correct characteristic value to rebuild the tampered regions. Furthermore, the proposed scheme authenticates the integrity of the image to the receiver and effectively recovers the tampered image.

Block truncation coding (BTC) is a class of image compression algorithms whose main technique is the partitioning of an image into pixel blocks that are then each encoded using a representative set of pixel values. It is commonly used because of its simplicity and low computational complexity. The Quadtree-segmented BTC (QTS-BTC), which utilizes a dynamic hierarchical segmentation technique, is among the most efficient in the BTC class. In this study, we propose a new BTC variant that introduces two ideas: (1) the use of a k -d tree for segmentation and (2) the use of a Mean Squared Error (MSE) threshold for dynamically determining the granularity of the blocks. We refer to this new BTC variant as the k -d Tree Segmented BTC (KTS-BTC), and we test this against some of the existing BTC variants by running the algorithms on a standard image compression dataset. The results show that the proposed variant yields low bit rates of the compressed images, even outperforming the state-of-the-art QTS-BTC, without a significant reduction in image quality as measured using the Peak Signal-to-Noise Ratio (PSNR). The utilization of k -d tree for image segmentation is further shown to have more impact than that of employing the MSE thresholding scheme as a block activity classifier.

To reduce frame memory usage in LCD overdrive, block truncation coding (BTC) is commonly used due to its efficient coding and low implementation cost. However, the inherent limitation of BTC causes severe perceptual artifacts and degrades overdrive performance. To solve this problem, we propose an adaptive multi-level BTC (AM-BTC) in this paper. The AM-BTC firstly overcomes the limitation by adaptively selecting 2-level or 4-level BTC according to the edge property of the coding block. Then, to reduce the bit rate of AM-BTC we improve the 2-level and 4-level BTCs by using only luminance bit-map to represent three color bit-maps. As shown in simulation results, the AM-BTC successfully reduces the frame memory usage to 1/6 and significantly improves coding performance (up to 3.779 dB) as compared with other algorithms. When the AM-BTC is applied to LCD overdrive, it also improves overdrive performance up to 3.390 dB as compared with other competitive methods in literature(1).

Vector quantization (VQ) is an important method of lossy image compression. The basic prerequisite of VQ is that the codebook must have representability to ensure the quality of the recovery image. In this paper, we propose a new codebook design approach using genetic algorithm. Our method will split a gray-level image into blocks of 4*4 size and simplify these blocks to enhance the representability of our codebook besides, we modify some GA operations to avoid illegal chromosomes. The experimental results show that our method has better representability and generalization than conventional method. As for the quality of recovery images, our method also outperforms the conventional method.

An effective watermarking technique using BTC and SVD for image authentication and quality recovery

The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time. Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle to involve four types of binary code books (i.e. Pour when , Flat when , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding procedure, with very small distortion per block, by designing small binary codebook, then rotating each block in it. Moreover, it can be used for improving the efficiency of the coding process even further with the decrease in the bit rate (i.e. increasing the compression ratio(.

We propose a low bit-rate coding algorithm for wireless communication based on multi-stage hierarchical vector quantization, motion compensation and differential pulse code modulation. Our method adapts bit allocation to spatial and temporal correlation. Conventional schemes based on the discrete cosine transform (DCT) need a large amount of computation on both encoding and decoding. On the other hand, our proposed method consists of addition, subtraction and shift operation. It does not use multiplication. It can decode in real time on a conventional serial processor. Encoding by vector quantization (VQ), however, consumes a large amount of computation. We developed a new LSI to accelerate VQ. Our scheme can send 10 frames of QCIF video sequences through a 29.2 kbps line. The quality of reconstructed image is over 30 dB.

This paper presents a hybrid image compression scheme based on a block based compression algorithm referred to as Vector Quantization (VQ) combined with the Differential Pulse Code Modulation (DPCM) technique. The proposed image compression technique called the PVQ scheme results in enhanced image quality as compared to the standalone VQ. The generated codebooks for the PVQ scheme are more robust for image coding than that of the VQ. This made our system a suitable candidate for developing on chip image sensor with integrated data compression processor. The proposed system was validated through FPGA implementation. The resulting implementation achieved good compression and image quality with moderate system complexity.

The sheer size of volume data sampled in a regular grid requires efficient lossless and lossy compression algorithms that allow for on-the-fly decompression during rendering. While all hardware assisted approaches are based on fixed bit rate block truncation coding, they suffer from degradation in regions of high variation while wasting space in homogeneous areas. On the other hand, vector quantization approaches using texture hardware achieve an even distribution of error in the entire volume at the cost of storing overlapping blocks or bricks. However, these approaches suffer from severe blocking artifacts that need to be smoothed over during rendering. In contrast to existing approaches, we propose to build a lossy compression scheme on top of a state-of-the-art lossless compression approach built on non-overlapping bricks by combining it with straight forward vector quantization. Due to efficient caching and load balancing, the rendering performance of our approach improves with the compression rate and can achieve interactive to real-time frame rates even at full HD resolution.

WITHDRAWN: Multi-classifiers and deep cnns for implementing image block truncation coding in e-learning platform