Interpolative three-level block truncation coding algorithm

Oshri, E.; Shelly, N.; Mitchell, H. B.

doi:10.1049/el:19930846

Cited by 13 publications

(5 citation statements)

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“…Several efforts [12][13][14][15][16][17][18] apply to the variety of image conversion or pixel dissimilarity or straightforward entropy coding. The input image was first divided into non overlapped blocks based on the predefined threshold values.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

2024

ICST Transactions on Scalable Information Systems

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The main aim of this research work is to compress grayscale images efficiently using prediction and intensity-based image compression algorithms. Image compression is useful for removing the duplication in an image to store and transmit the data in an efficient form. This research work analyzes four new schemes for gray scale lossy image compression. Among the four schemes considered, two compressive approaches are designed for Prediction Based Image Compression (PBIC) level implementation. Third approach is designed for Intensity Based Image Compression (IBIC). Finally, the previously designed PBIC and IBIC schemes lead to an Integrated Encoder. All the considered method performances are analyzed using the performance metrics. These results are compared with JPEG 2000 which is a extensively used benchmark compression encoder. The outcome of all the proposed methods is also compared with modern encoders.

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Section: Literature Reviewmentioning

confidence: 99%

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

2024

ICST Transactions on Scalable Information Systems

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“…In [26], the image pixels are predicted by a hierarchical prediction scheme and then the wavelet transform is applied to the prediction error. Some work [5,9,11,[27][28][29][30] applies various types of image transformation or pixel difference or simple entropy coding. An image transformation scheme known as "J bit encoding" (JBE) has been proposed in [11].…”

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confidence: 99%

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Kabir

Mondal

2018

J. Imaging

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Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an "edge-based transformation and entropy coding" (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed "prediction-based transformation and entropy coding" (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: "joint photographic experts group lossless" (JPEG-LS), "set partitioning in hierarchical trees" (SPIHT) and "differential pulse code modulation" (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.compression are some of the wavelet-based compressions such as embedded zerotrees of wavelet transforms (EZW), joint photographic experts group (JPEG) and the moving picture experts group (MPEG) compression.A large number of research papers report image compression algorithms. For example, one study [13] is about discrete cosine transform (DCT)-based lossless image compression where the higher energy coefficients in each block are quantized. Next, an inverse DCT is performed only on the quantized coefficients. The resultant pixel values are in the 2-D spatial domain. The pixel values of two neighboring regions are then subtracted to obtain residual error sequence. The error sequence is encoded by an entropy coder such as Arithmetic or Huffman coding [13]. Image compression in the frequency domain using wavelets is reported in several studies [12,[14][15][16][17]]. In the method described in [14] lifting-based bi-orthogonal wavelet transform is used which produces coefficients that can be rounded without any loss of data. In the work of [18] wavelet transform limits the image energy within fewer coefficients which are encoded by "set partitioning in hierarchical trees" (SPIHT) algorithm.In [19] JPEG lossless (JPEG-LS), a prediction-based lossless scheme, is proposed for continuous tone images. In [14] embedded zero tree coding (EZW) method is proposed based on the zero tree hypothesis. The study in [12] proposes a compression algorithm based on combination of discrete wavelet transform ...

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“…The main drawback of this algorithm is its high bit rate. However, it is an attractive option due to its simplicity and high image quality (Oshri et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…Some of these algorithms use the same two level quantizer, while others use a three-level quantizer (Wang et al, 2009a;Oshri et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…In (Oshri et al, 1993), the authors used a three level quantizer to classify the pixels into low, intermediate and high intensity, according to a predefined threshold. To further reduce the bit rate, this algorithm transmitted only an odd row and odd column of each (6×8) block; the other pixels were reconstructed according to the Laplacian interpolation function.…”

Section: Introductionmentioning

confidence: 99%

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Image Compression Algorithms Using Intensity Based Adaptive Quantization Coding

Al-Azawi¹,

Boussakta²,

Yakovlev³

2011

American Journal of Engineering and Applied Sciences

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Problem statement: Low complexity image compression algorithms are necessary for modern portable devices such as mobile phones, wireless sensor networks and high constraint power consumption devices. In such applications low bit rate along with an acceptable image quality are an essential requirements. Approach: This study proposes low and moderate complexity algorithms for colour image compression. Two algorithms will be presented; the first one is intensity based adaptive quantization coding, while the second is a combination of discrete wavelet transforms and the intensity based adaptive quantization coding algorithm. Adaptive quantization coding produces a good Peak Signal to Noise Ratio (PSNR), but with high bit rates compared with other low complex algorithms. The presented algorithms produce low bit rate whilst preserving the PSNR and image quality at an acceptable range. Results: Experiments were performed using different kinds of standard colour images, a multi level quantizer, different thresholds, different block sizes and different wavelet filters. Both algorithms considered the intensity variation of each colour plane. At high compression ratios the proposed algorithms produced 1-3 bpp bit rate reduction against the stand alone adaptive quantization coding for the same image quality. This reduction was achieved due to dropping of some blocks that claimed to be low intensity variation according to a comparison with predefined thresholds for each colour plane. The results show that the bit rate can be reduced by 72-88% for each low variation image block from the original bit rate. Conclusion: The results obtained show a good reduction in bit rate with the same PSNR, or a slightly less than PSNR of a standalone adaptive quantization coding algorithm. Further bit rate reduction has been achieved by decomposing the input image using different wavelet filters and intensity based adaptive quantization coding. The proposed algorithm comprises a number of parameters to control the performance of the compressed images.

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Interpolative three-level block truncation coding algorithm

Cited by 13 publications

References 10 publications

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

Empirical Evaluation of Coding and Inter Pixel Redundancy in still Image Compression

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

Image Compression Algorithms Using Intensity Based Adaptive Quantization Coding

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