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Read image 2. Extract RGB values of each pixel 3. Compute threshold as average of max and min values 5. Divide R matrix into blocks of 4x4 6. Similarly perform steps for each plane. Algorithm 2 Level Block Truncation Coding 1. Calculate mean of each 4x4 block 2. Generate bitmap based on the means 3. Construct new compressed matrix based on RV0, RV1 values. Algorithm 4 Level Block Truncation Coding 1. Calculate M, R dynamic range values for threshold calculation for each 4x4 block formula given in code 2.

Generate bitmap based on T1, T2, T3 values i. Skip to content. Star 2. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In this section, these important techniques are reviewed in details. Block truncation coding BTC was proposed for grayscale-image compression. The basic steps of BTC can be summarized as follows. Step 1. Then the mean value u of each block is computed by using Eq.

Step 2. The pixels of the block are classified into two sets. We denoted the number of pixels in set H and set L as nH and nL, respectively. So the high value xH and the low value xL are defined as:. Step 4. Finally, the bitmap M, the high value xH and the low value xL are combined together to reconstruct the original pixel values for each block. If the value of mij is 1, the value of the restored pixel at the position i, j would be xH. Otherwise, the value of the restored pixel would be xL.

The mean value u of the image block is computed as and the high value xH and the low value xL are computed as and 62, respectively. The bitmap for this image block is generated, as shown in Fig. Example for BTC encoding and decoding, a an original image block, b bitmap, c the reconstructed image block. First, a color image is divided into non-overlapping blocks and each block is compressed by BTC, producing three bitmaps and three pairs of quantization values for the three channels, i.

Then, the common bitmap is generated from these three bitmaps, as shown in Figs. If the bits in the same positions of the three bitmaps are equal, we put them into the common bitmap at the corresponding positions and denote them as determined elements. The initial common bitmap only contains the determined elements, which is shown in Fig. The non-determined elements are determined by the following steps [23]. Example of the initial common bitmap, a Bitmap of R channel, b Bitmap of G channel, c Bitmap of B channel, d The initial common bitmap.

Pick an element from the non-determined elements as the determined element and compute two new MSEs by using Eq. According to the minimum new MSE, determine which value of the element should be selected. Then, put the corresponding value into the common bitmap as the determined element. Step 3. Repeat Step 2 until all of the non-determined elements have been determined; at this moment, the common bitmap is generated.

Ant Colony Optimization ACO , proposed by Dorigo and Gambardella [], is a kind of simulated evolutionary algorithm based on the way ants find food. Following the idea of ACO, the BACO algorithm performs competitively in solving the discrete-domain problems because of its unique space structure of random binary search. As shown in Fig. Initialize a population of ants with the same pheromones.

The ant selects 0 or 1 according to a probability related to its own pheromone:. A solution in a binary bit stream is generated after each ant has searched all nodes. The circulation continues until the end condition is met. In this way, we can obtain an approximate optimum solution. In this paper, we propose a novel image compression scheme for color images based on BACO.

The main idea of our scheme is to generate a near-optimal common bitmap to replace the three individual bitmaps that are generated from different channels by BTC. Then every block can obtain three pairs of quantization values for the three channels, i. Our goal was to achieve a common bitmap for a color image with minimal distortion of the entire color image.

BACO was used to achieve this goal. This process is detailed as follows. Input: Original color image block BL, three pairs of quantization values, i. Select a value of matrix C and change it into the opposite value to generate a temporary matrix. In this matrix, only the selected value is different from the values in the matrix C.

Compute the corresponding newMSEij for this matrix by using Eq. Step 6. Repeat Step 3 through Step 5 until all of the values in matrix C are dealt with. Similarly, all of the image blocks are dealt with in the same way. Considering that each position of the common bitmap has an independent effect on the distortion of a block, we assigned an ant for each position instead of assigning an ant to search all the positions in order to reduce the time required to determine the proper common bitmap.

For a common bitmap, each position has only two choices, 0 or 1. Each ant is initialized to 0 or 1 randomly and an initial common bitmap is generated when all the ants has been initialized. Then select an ant each time and change its value to the opposite. The pheromone of this ant for choosing 0 or 1 is changed by computing new MSE for the corresponding block.

By comparing the updated pheromone values of this ant, whether choosing 0 or 1 will be determined for this ant. When the pheromone of all the ants are updated, the near-optimal solution will be generated according to the updated pheromone matrices.

Therefore, this process will not get into local optimum. All of the initial pheromone values are 0. The three new pairs of quantization values are denoted as for the three R, G, and B channels, respectively, as shown in Eq. The restoration process is the inverse process of the compression process.

The original color image can be restored based on the generated common bitmap and the three pairs of quantization values. Example of restoring an image block, a the common bitmap, b The restored image block of R channel, c The restored image block of G channel, d The restored image block of B channel. In this section, some experimental results are presented to prove the feasibility of the proposed method.

The simulation environment of our experiments was a PC with a 2. The peak signal-to-noise ratio PSNR is used to measure the visual quality between the reconstructed color image and the original color image, and it was defined as follows:. Table 1 shows that average PSNR value of the proposed method for six images is This illustrates that the proposed scheme obtained better quality recovered images.

The data in Table 2 also confirms this result. This was due to the fact that larger block sizes worsen the scale of distortion of the recovered pixels. However, the proposed scheme still obtained higher PSNR values than the other schemes, proving that our algorithm provided a more approximate common bitmap.

The structural similarity SSIM index is used to measuring the similarity between two images. The SSIM index is a decimal ranging from -1 to 1. Only when two images are identical, a value of 1 is reachable. It was defined as follows:. From Tables 3 and 4, it is obvious that the average SSIM values of our scheme are higher than other schemes, which confirms that our scheme is better. Time consumption is another factor that can be used to evaluate the performance of a scheme.

When an element from the non-determined group is selected and moved into the determined group, the mean value changes. As a result, two new MSE values must be computed for each element in order to determine which value should be selected. However, in our algorithm, the predetermined quantization values can be used to compute MSE so that we just need compute a new MSE only once for each element, making our algorithm less complex than GSBTC. These experimental results indicated that our scheme took the least amount of time while its PSNR values were the highest among the compared schemes.

Therefore, the proposed scheme is more efficient than the other related schemes. The blue column represents PSNR values while the orange column represents the time consumption. From the below figures, it is obvious that our scheme achieves higher PSNR than other schemes.

Meanwhile, the proposed scheme is efficient while keeping high PSNR values. A novel color image compression scheme based on BACO is proposed in this paper to compress a color image efficiently. First, the color image was compressed by traditional BTC to get three pairs of quantization values for each block. Then the common bitmap was generated to replace the original three bitmaps in order to improve the compression ratio.

In our algorithm, BACO was used to generate a near- optimum common bitmap and the three pairs of quantization values were readjusted to reduce the distortion of the reconstructed image. The original image can be restored by using the common bitmap and the corresponding quantization values. The experimental results confirmed that the reconstructed images had better visual quality and consumed less time, which indicated that our scheme is efficient for obtaining an appropriate common bitmap and will be beneficial for the transmission and storage of color images.

In the future, our method can be used in sharing color images and in other fields due to its efficiency and high visual quality. Click here to choose a searching target image or drag and drop a searching target image. Article Info. Abstract For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding BTC of the R, G and B channels.

Introduction With the development of the Internet, digital images are transmitted extensively and used in many fields, such as multimedia systems, electronic publishing, and communications. So the high value xH and the low value xL are defined as: Step 3. Example for BTC encoding and decoding, a an original image block, b bitmap, c the reconstructed image block 2.

Flowchart of our algorithm 3. Step 5. Update the common bitmap according to the following rule. Example of restoring an image block, a the common bitmap, b The restored image block of R channel, c The restored image block of G channel, d The restored image block of B channel 4. Experiments and Discussions In this section, some experimental results are presented to prove the feasibility of the proposed method.

Table 1. Table 3.

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Face Recognition. Iris Recognition. Speech Recognition. Deep Script. Image Compression. Raspberry PI Remote Desktop. Software Info About us. Figure 1. JPEG image. Demo code protected P-files available for performance evaluation. Matlab Image Processing Toolbox is required. We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. Once you have done this, please email us luigi.

Alternatively, you can bestow using our banking coordinates: Name :. New - Python Face Recognition. Proposed algorithm results computationally inexpensive and it can run also in a low-cost pc such as Raspberry PI. New - Speaker Verification System. Text-Independent Speaker Authentication There are two major applications of speaker recognition technologies and methodologies. If the speaker claims to be of a certain identity and the voice is used to verify this claim, this is called verification or authentication.

New - Java Face Recognition. Java-based Biometric Authentication System Face recognition is essential in many applications, including mugshot matching, surveillance, access control and personal identification, and forensic and law enforcement applications. New - White Papers. The bits of watermark are grouped together and for each sequence a different modulation coefficient is used. New - WebCam Face Identification. Face Recognition Based on Fractional Gaussian Derivatives Local photometric descriptors computed for interest regions have proven to be very successful in applications such as wide baseline matching, object recognition, texture recognition, image retrieval, robot localization, video data mining, building panoramas, and recognition of object categories.

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Octave Face Identification. PHP Face Identification. Java Face Recognition. I have made a flexible function, myim2col. The inverse function, mycol2im. To do the lifting scheme for wavelets myim2col. This works essentially like the Matlab Wavelet Toolbox function lwt2. A wavelet can also be implemented using a matrix representing the wavelet as an overlapping transform, to get this matrix I made the function GetWave. Also note that the implementation of overlapping transform in mycol2im.

Examples of how to use mycol2im. See help text for mycol2im. In its basic form quantization is simply rounding the real values of the coefficients into nearest integers. More generally scalar quantization is a mapping from a continuous interval into integers, i.

Quantization removes information accuracy from the numbers and thus introduces error during reconstruction; the reconstructed value is usually the bin center. Often more advanced ways of quantization are used. JPEG scales each sub-band by a factor before rounding to integers, the factors are chosen considering the human visual system which is more sensitive to low-frequency noise than high frequency noise.

JPEG may vary the quantization step size for each sub-band, and use a larger zero-bin. Also, truncation during bit-plane coding may be considered as a form of quantization. The examples here use a simple uniform quantization with thresholding, i. Usually, the zero-bin is twice the size of other bins.

The implementation is in uniquant. Having the original image A including the peak , for 8 bit pixel values this is , and the reconstructed image Ar PSNR can be found as. The matrix Z after scalar quantization is of same size as the matrix X after transform, and its elements are integers. This matrix should be coded without loss. If the transform is appropriate for the image the Z matrix will have a structure, many elements will be zeros, and these zeros will often be clustered. A good coding strategy takes advantage of this in an effective way.

Here, the coding is done in two steps. First reshaping the Z matrix into sequences of integers, and then these sequences are entropy coded. A large number of techniques are available when building these sequences.

First row of the Z matrix should contain the DC component of the image after the transform. This is the most important sub-band for natural images, and represents by itself a crude representation of the image. Since neighboring pixels in an image often have almost the same values, difference coding DPCM of this row may be helpful, i.

This coding can be reversed without loss. Two small functions can do this:. Since the DC sub-band, first row of Z , represents a crude representation of the image it has a two-dimensional structure like an image. Prediction of each new element pixel can then be made more complex, and better, than simple DPCM by exploiting the two dimensional structure.

It is also possible, and helpful, to predict not only the value of next element but also the magnitude, or variance, of the prediction error. The prediction errors with small estimated variance can be put in one sequence and the prediction errors with larger estimated variance in another sequence.

The resulting sequences will probably have different distribution of the symbols integers and different entropy , and fewer bits are needed to code the two, or more, sequences of prediction errors than what is needed to code one sequence with all the prediction errors. I have made a Matlab function that uses this principle, mypred. The prediction scheme can be extended and used on the image itself; this can give quite effective lossless image coding schemes.

CALIC is quite complicated, with four major components: 1 Linear prediction, 2 Context selection by estimating the error energy level, 3 Modeling of the prediction error and adjusting it by subtracting a bias, making the final prediction non-linear.

I have made a Matlab function that is like the first two parts of CALIC, and that return 8 one for each context integer sequences, calic. The non-DC sub-bands, all rows of Z except first, may be coded individually or block-wise. The structure of Z is often so that column-wise coding of the AC components is best, since each column represent a localized image block. To do this effectively the rows of Z should be ordered by ascending frequency, this order is given by the transformation.

An alternative, which works quite well if there is not too many rows is to sort the rows by the number of non-zeros in each row. Then this order must be included as an additional integer sequence. Arranging the elements from the matrix into one sequence column-wise gives a sequence with the same number of elements, but using an EOB symbol may significantly reduces the number of elements since the zeros often will be at the end of each column.

The resulting sequence can further be divided into three sequences depending on the previous symbol, zero, EOB or other. This often improves coding results because after a zero there will never be an EOB symbol, and after an EOB-symbol there will only be symbols from the first row second row if DC is handled otherwise or possible EOB symbols. Run-Length-Encoding RLE can be used to better code sequences with many zeros or another frequent value , especially if this value tends to be clustered.

This is often the case with the EOB-sequence of symbols following a zero. One way to do this is to introduce a new symbol, Run-Of-Zeros, which mean two or more zeros. Alternatively it could mean one or more zeros. This symbol is then inserted in the sequence everywhere a run of zeros is present, and the run of zeros is removed.

The number of zeros in each run of zeros must be stored in a new sequence. The first sequence may further be divided into two sequences depending on the previous symbol, since we know that after a zero or a Run-Of-Zeros symbol there will always be a non-zero value. A third method that is efficient if there are many zeros is to make two sequences from the sparse matrix, one sequence with the non-zero values and one with the indexes DPCM coded.

Combining these methods in an effective way may reshape the matrix into sequences that are easier to effectively compress, three variants are implemented in myreshape. Many years ago I implemented Huffman coding and some simple arithmetic coding schemes in Matlab.

These functions still works ok, see my Entropy Coding page. They just make the sequences of integers into a sequence of bytes the bit stream , no packing is done to group the bit stream into independent parts as is an important feature of many image compression schemes. I have also made a function that, if Huff

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BTC is a recent technique used for compression of monochrome image data. It is one-bit adaptive moment- preserving quantizer that preserves certain statistical moments of small blocks of the input image in the quantized output.

The original algorithm of BTC preserves the standard mean and the standard deviation [3]. The statistical overheads Mean and the Standard deviation are to be coded as part of the block. The truncated block of the BTC is the one-bit output of the quantizer for every pixel in the block.

BTC is simple algorithm for lossy compression but quality image compression for litte size image not really good [2]. We modified the algorithm BTC would smooth and clear. Various methods have been proposed during last twenty years for image compression such BTC. Section 3, explains about flowchart BTC algorithm in matlab, for section 4, and explains about implementation result and the last section is conclusion for this paper.

The trade off between data compression remains one of the difficult problems. Maintaining high compression ratios with good image quality is possible at a more or less high computational cost. One of the main goals for image data compression is to reduce redundancy in the image block a much as possible. That is, it is very important to represent an image with as few bits as possible while maintaining good image quality. Both compression and decompression algorithms should be simple and efficient.

BTC is one of the simple and easy to implement image compression algorithms. This part introduces the BTC coding algorithm In BTC an image is segmented into nn typically,44 nonover lapping blocks of pixels, and a two-level one-bit quantize is independently designed for each block. Both the quantize threshold and the two reconstruction levels are varied in response to the local statistics of a block [3][7][11].

The level of each block is chosen such that the first two sample moment is preserved. Xm be the pixel value in a given block of the original image. Taking x as the threshold value a two-level bit plane is obtained by comparing each pixel value xi with the threshold. A binary block, denoted by B, is also used to represent the pixels. Once image compression system has been designed and implemented, it is important tobe able to evaluate its performance.

This evaluation should be done in such a way to be able to compare results against otherimage compression techniques. The image quality metrics can be broadly classified into two categories, subjective and objective.

Subjective image quality is a method of evaluationof images by the viewers read images directly to determine their quality. In objective measures of image quality metrics, some statistical indices are calculated to indicate the image quality. It is an attractive measure for the loss of image quality due to its simplicity and mathematical convenience. PSNR is dimensionless and is expressed in decibel [4]. PSNR is defined as follow: Where L is the dynamic range of the pixel values for 8-bit grayscale images.

They are x pixels with 5 images and x pixels with 5 images. Figure 2 and Figure 3 are shown the database images are used in experiment. Before experiment, we must design the algorithm works shown in flowchart and the algorithm with posecode. For detail we can know in figure 1. You can follow this link Huffman coding and Algorithm.

The source reduction and code assignment procedures just described are implemented by using the following M function. After creating the M file for implementing the huffman code. This function efficiently produce the huffman code for a given input vector. If any of the step in the coding is not clear to you. You are commenting using your WordPress.

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Home About « Hello world! Hello readers!!! Interpixel redundancy : It arises due to the correlation between the pixels of an Image. PsychoVisual redundancy : It is due to the data ignored by the human visual system. Huffman Code: When coding the grey level intensity of an image or the output of the grey-level mapping operation, Huffman codes contains the smallest possible number of code symbols e.

Will meet you guys next time with more on Image processing using the Matlab. Have a good day!!!! Share this: Share Facebook. Like this: Like Loading Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:. Email required Address never made public. Name required.

Block Truncation Coding is a type of lossy image compression technique for greyscale images. It divides the original images into blocks and then uses a. Hi all, I need to write a matlab program to compress an image using the block truncation method and cannot use toolboxes or addons. Block Truncation Coding (BTC): To my knowledge, BTC is the simplest approach to perform image compression. BTC- Implementation: As provided in.