Block Truncation Coding Research Articles

Flicker-reduced memory compression for a volume-zone liquid crystal display overdrive

The memory compression algorithm CCC-LCP (color-count-controlled local color palette) reduces flicker in the liquid crystal display (LCD) overdrive. Its compression ratio is 1:5.4 for 10-bit images, with a 33 dB PSNR peak signal-to-noise ratio and with five times flicker reduction compared with the block truncation coding. The authors’ two-alternative forced choice subjective tests proposed two new soundness properties, the ‘CMP harmlessness’ and ‘OD non-lost (or OD liveliness)’, to clarify the functional interaction between the overdrive functionality OD and the compression functionality CMP. The tests verified that CCC-LCP is practically applicable (at a 1.2H viewing distance threshold) for 42” 37-ppi WXGA TVs.

Block Truncation Coding with Huffman Coding

Block Truncation Coding with Huffman Coding

Color Image Compression Based on Absolute Moment Block Truncation Coding Using Huffman Coding

This paper describes a color image compression scheme based on absolute moment block truncation coding (AMBTC) using Huffman coding. High degree of correlation between the RGB planes of a color image is reduced by transforming them to Y, Cb, Cr planes. Each plane is then encoded separately using the proposed method. The experiment has been conducted using the proposed scheme on standard color images. Experimental results show that the compression ratio of the proposed method is significantly improved when compared to color BTC and color AMBTC, with a relative distortion of reconstructed images.

Hierarchical Method for Foreground Detection Using Codebook Model

This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.

PARAMETRIC EVALUATION ON THE PERFORMANCE OF VARIOUS IMAGE COMPRESSION ALGORITHMS

Wavelet analysis plays a vital role in the signal processing especially in image compression. In this paper, various compression algorithms like block truncation coding, EZW and SPIHT are studied and analyzed; its algorithm idea and steps are given. The parameters for all these algorithms are analyzed and the best parameter for each of these compression algorithms is found out

Improved Adaptive Block Truncation Coding for Image Compression

ABSTRACT The Block Truncation Coding (BTC) is one of the lossy image compression algorithms. In this paper, we have proposed a method called the Improved Adaptive Block Truncation Coding (IABTC) based on Adaptive Block Truncation Coding (ABTC). The feature of inter-pixel redundancy is exploited to reduce the bit-rate further by retaining the quality of the reconstructed images. The proposed method outperforms the existing BTC based methods both in terms of bit-rate and PSNR values. General Terms Image Compression Keywords Compression, block, bit plane, bpp, PSNR, quantizers 1. INTRODUCTION Data compression algorithms lead to reduction in transmission time and storage cost. Image compression techniques are classified into lossy and lossless techniques. In lossless techniques, the reconstructed image will be the exact replica of the original image whereas in lossy techniques, the reconstructed image will be an approximation of the original image. One of the main goals for image data compression is to reduce redundancy in the image block as much as possible. That is, it is very important to represent an image with as few bits as possible while maintaining good image quality. xiBoth compression and decompression algorithms should be simple and efficient. BTC is one of the simple and easy to implement image compression algorithms [1]. It comes under lossy image compression techniques. Though some data is lost, it does not make much difference to the Human Visual System (HVS). is the original pixel value of the block. One such lossy image compression techniques is Block Truncation Coding (BTC) technique. BTC has the advantage of being easy to implement when or 0s for each block is called acompared to Vector Quantization [8] and Transform Coding [9, statistical moments 10]. BTC achieves 2 bits per pixel (bpp) with low computational complexity [11]. Many techniques with modifications to BTC have been proposed to reduce the bit-rate obtained with normal BTC: Gallagher [12] proposed a Median Filter Roots method; Yung-Gi Wu [13] proposed Probability based Block Truncation Image Bitplane Coding; Yu-Chen Hu [14] presented a Modified BTC with Predictive Technique and Bitplane Coding with Edge Pattern. In this paper, we have presented a method named Improved Adaptive Block Truncation Coding (IABTC) based on the Adaptive Block Truncation Coding (ABTC) method. Few other existing Block Truncation Coding (BTC) based methods are discussed in Section 2. The proposed method is explained in Section 3. Results are discussed in Section 4 and the conclusion is given in Section 5.

Improved differential pulse code modulation-block truncation coding method adopting two-level mean squared error near-optimal quantizers

The conventional hybrid method of block truncation coding (BTC) and differential pulse code modulation (DPCM), namely the DPCM-BTC method, offers better rate-distortion performance than the standard BTC. However, the quantization error in the hybrid method is easily increased for large block sizes due to the use of two representative levels in BTC. In this paper, we first derive a bivariate quadratic function representing the mean squared error (MSE) between the original block and the block reconstructed in the DPCM framework. The near-optimal representatives obtained by quantizing the minimum of the derived function can prevent the rapid increase of the quantization error. Experimental results show that the proposed method improves peak signal-to-noise ratio performance by up to 2dB at 1.5 bit/pixel (bpp) and by 1.2dB even at a low bit rate of 1.1 bpp as compared with the DPCM-BTC method without optimization. Even with the additional computation for the quantizer optimization, the computational complexity of the proposed method is still much lower than those of transform-based compression techniques.

A Simple Image Encoding Method with Data Lossless Information Hiding

In this paper, we propose a simple reversible data hiding method in the spatial domain for block truncation coding (BTC) compressed grayscale images . The BTC method compresses a block of a grayscale image to a bitmap and a pair of quantization numbers. The proposed method first embeds secret bits into a block b y chang ing the order of those two quantization numbers. The compression rate is not enlarged by this embedding scheme. To further improve the hiding capacity, the proposed method embeds the secret bits into the bitmap of the block by exploiting the correlation characteristic of neighboring block s ’ bitmap in natural images. The experimental results show that the proposed method achieves very good hiding capacity at low compression rate.

Joint Secret Sharing and Data Hiding for Block Truncation Coding Compressed Image Transmission

Joint Secret Sharing and Data Hiding for Block Truncation Coding Compressed Image Transmission

Ratio‐modified block truncation coding algorithms for reduced bitrates

Block truncation coding (BTC) technique is a simple and fast image compression algorithm since complicated transforms are not used. The principle used in BTC algorithm is to use two‐level quantiser that adapts to local properties of the image while preserving the first‐ or first‐ and second‐order statistical moments. The parameters transmitter or stored in the BTC algorithm are statistical moments and bitplane yielding good quality images at a bitrate of 2 bits per pixel (bpp). In this paper, two algorithms for modified BTC (MBTC) are proposed for reducing the bitrate below 2 bpp. The principal used in the proposed algorithms is to use the ratio of moments which is a smaller value when compared to absolute moments. The ratio values are then entropy coded. The bitplane is also coded to remove the correlation among the bits. The proposed algorithms are compared with MBTC and the algorithms obtained by combining JPEG standard with MBTC in terms of bitrate, peak signal‐to‐noise ratio (PSNR) and subjective quality. It is found that the reconstructed images obtained using the proposed algorithms yield better results.

Colour image retrieval using pattern co-occurrence matrices based on BTC and VQ

Proposed is an effective feature for colour image retrieval based on block truncation coding (BTC) and vector quantisation (VQ). Each input colour image is decomposed into Y, Cb and Cr components. BTC is performed on the 4×4 Y blocks, obtaining a mean pair sequence and a bitplane sequence, and then they are quantised with the contrast pattern codebook and visual pattern codebook to obtain the contrast and visual pattern co-occurrence matrix. VQ is performed on the 4×4 Cb blocks and Cr blocks with the Cb codebook and Cr codebook, respectively, to obtain the colour pattern co-occurrence matrix. Retrieval simulation results show that, compared with two existing BTC-based features, the proposed feature can greatly improve retrieval performance.

Reversible Steganography for BTC-compressed Images

Reversible steganography becomes a popular hiding problem in recent years. A reversible steganographicmethod can reconstruct an original image without loss from the stego-image after extracting the embedded data. Unlike traditional reversible methods in which data is hidden in uncompressed images, we propose a reversible scheme for BTC (block truncation coding)-compressed images. The secret data embedded in the compressed image are more difficult to detect than in the uncompressed image. To achieve reversibility, the properties of side matching and BTC-compressed code are applied. The experimental results show that the proposed method is feasible for BTC-compressed images and can embed one more bit in each BTC-encoded block.

Multilevel Compression Scheme using Vector Quantization for Image Compression

In this paper, we have proposed a multi level compression scheme, in which the initial codebook and the Index Map that are generated using the normal Vector Quantization are compressed in three levels. The Minimum Mean Square method that is adopted in Block Truncation Coding is used to compress the codebook in first level with some improvements. The interpolative method is used to perform second level of compression to the codebook. Finally the Search Order Coding method is used to compress the Index Map without loss of image data. The experimental results show that there is a significant improvement in bit reduction without loosing much of image data.

Fast and Low Bit-Rate AMBTC Image Compression Scheme

In this paper, a low bit rate image compression scheme based on the absolute moment block truncation coding (AMBTC) is presented. In this scheme, a combination of three techniques, four-level block prediction technique, residual block coding technique and interpolative bit plane coding technique, are used. Experimental results show that the proposed scheme achieves better image quality at low bit rate with less computational cost than the existing low bit rate scheme.

Multipurpose Perceptual Image Hashing Based on Block Truncation Coding

Multipurpose Perceptual Image Hashing Based on Block Truncation Coding

Image Retrieval with Shape Features Extracted using Morphological Operators with BTC

ABSTRACT The paper discusses novel image retrieval methods based on edge texture of images extracted using morphological operators. The existing CBIR techniques are based on the feature vectors extracted from morphological edge extraction techniques such as simple morphological edge extraction technique, Top-Hat transform and Bottom-Hat transform. The proposed CBIR techniques are using the morphological edge extraction techniques with block truncation coding (BTC). The proposed techniques are tested on generic image database with 1000 images spread across 11 categories. In all 55 queries (5 from each category) are fired on the image database. The average precision and recall of all queries are computed and considered for performance analysis. The experimental results show that use of BTC over morphological shape images for feature extraction improves the performance of image retrieval with reduced computational complexity for query execution. In all BTC with simple morphological edge extraction based CBIR method (SMBTC) gives best performance.

On-chip frame memory reduction using a high-compression-ratio codec in the overdrives of liquid-crystal displays

Overdrive is commonly used to reduce the liquid-crystal re- sponse time and motion blur in liquid-crystal displays (LCDs). However, overdrive requires a large frame memory in order to store the previous frame for reference. In this paper, a high-compression-ratio codec is pre- sented to compress the image data stored in the on-chip frame mem- ory so that only 1 Mbit of on-chip memory is required in the LCD over- drives of mobile devices. The proposed algorithm further compresses the color bitmaps and representative values (RVs) resulting from the block truncation coding (BTC). The color bitmaps are represented by a lumi- nance bitmap, which is further reduced and reconstructed using median filter interpolation in the decoder, while the RVs are compressed using adaptive quantization coding (AQC). Interpolation and AQC can provide three-level compression, which leads to 16 combinations. Using a rate- distortion analysis, we select the three optimal schemes to compress the image data for video graphics array (VGA), wide-VGA LCD, and standard- definitionTV applications. Our simulation results demonstrate that the pro- posed schemes outperform interpolation BTC both in PSNR (by 1.479 to 2.205 dB) and in subjective visual quality. C 2010 Society of Photo-Optical Instru-

Cost Effective Block Truncation Coding for Color Image Compression

In this paper, a cost effective block truncation coding (CE-BTC) using low cost approach is presented for color image compression. To reduce the bit rate of the basic BTC, selected single bit-map is employed to represent the edge information of the coding block in the proposed CE-BTC. Moreover, a previous proposed algorithm, adaptive quantization coding (AQC), is used to compress the two representative levels further. In order to achieve low cost, the CE-BTC reduces the line buffer memory usage to a half with the limited PSNR losing of 0.02dB compared with the optimal approach. Simulation results show that the proposed CE-BTC outperforms vector quantization-BTC up to 3 dB in PSNR. The comparison with our previous research AHIC also shows that an improvement of 0.425 dB in average PSNR is achieved by the CE-BTC.

Image Retrieval with Shape Features Extracted using Gradient Operators and Slope Magnitude Technique with BTC

The paper discusses novel image retrieval methods based on shape features extracted using gradient operators and slope magnitude technique with Block Truncation Coding (BTC). Four variations of proposed „Mask-Shape-BTC‟ image retrieval techniques are proposed using gradient masks like Robert, Sobel, Prewitt and Canny. The proposed image retrieval techniques are tested on generic image database with 1000 images spread across 11 categories. In all 55 queries (5 from each category) are fired on the image database. The average precision and recall of all queries are computed and considered for performance analysis. In all the considered gradient operators for shape extraction, „Mask-ShapeBTC‟ CBIR techniques outperform the „Mask-Shape‟ CBIR techniques. The performance ranking of the masks for proposed image retrieval methods can be listed as Robert (best performance), Prewitt, Sobel and lastly the Canny.

Parallel and element-reduced error-diffused block truncation coding

Block Truncation Coding (BTC) is an efficient compression technique for its inherent simple coding strategy. However, the annoying blocking effect and false contour accompanied in high coding gain configurations make the applications relatively limited compares to some up-to-date compression schemes. For this, Error-Diffused Block Truncation Coding (EDBTC) is proposed to solve these problems and obtain satisfactory results. Unfortunately, the EDBTC sacrifices the parallel advantage of traditional BTC. Moreover, the number of diffused directions of EDBTC can be reduced to obtain higher efficiency. For these, the Interlaced Error-Diffused Block Truncation Coding (IEDBTC) is proposed in this work to claim back the parallel advantage. In addition, the diffused elements are also reduced from four to two with the proposed optimization procedure while preserving the image quality.