DCT is an orthogonal transformation that is used for decorrelating image data. Encoding each transform coefficient is easy after using DCT without losing compression efficiency.

The quantization table option influences the compression ratio and entropy. In a lossy compression algorithm, the process of quantization is referred to throughout a stage.

The 2-D DCT is specified in Eq. (1):

(1) F(u,v)=2NC(U)C(V)∑ x=1N-1∑ y=1N-1f(x,y)cos[(π2x+1)u2N]cos[(π2x+1)u2N]

For u=0,…,N-1 and v=0,…,N-1

Coding Scheme

Compression Process

First of all, the whole image is loaded into the encoder, then transformed from RGB to YCBCR. The entire image is then divided into tiny NXN image blocks. From top to bottom or left to right, there are two directions where every block is applied to the DCT.

By dividing each transformed data item by the corresponding pixel in the quantization matrix Q, and rounding it to the nearest integer value, as shown in Eq. 2, the recompressed components of each block are obtained.

(2) F(u,v)Quantization=round[F(u,v)Q(u,v)]

The 1D array is extracted and transmitted to the receiver from this the encoding process is completed [4]. Extra compression can be achieved by applying acceptable entropy encoding. The de-quantized matrix is displayed in Eq. 3 and then using the 2-D inverse DCT.

(3) F(u,v)deQ=F(u,v)QuantizationXQ(u,v)

Decompression Procedure

The image reconstruction, the quantified DCT coefficients, are decoded and the inverse 2DDCT of the computer block is computed, then the blocks are collected in one image together. There are two important steps in this decoding process when applying the dequantization matrix to blocks: Maintaining the size of the block equals that used in the encoding method.

For turning the coordinate system, wavelets are “a mathematical tool.” For applications where tolerable degradation and scalability are significant, they are suitable.

Multiresolution Concept and Analysis

The representation of signals is designed by the multi-resolution concept When a single occurrence is broken down into smaller, smaller specifics [5,6].

Filter Bank

It is a collection of filters. Synthesis and analysis banks are components of it. It is represented in Fig. 1

Analysis Bank

It has two filters: A high and a low pass [6]. HH, LH, LL, and HL are four bands that are produced from this passing. Those 4 bands are the decomposition’s first level. Fig. 2 illustrated finer scale and coarser scale wavelet coefficients.

Synthesis Bank

That is the reverse of analytics bank collection. This included filtration and decimation.

Coding Scheme

Compression Process

Passing image through LPF and HPF and the Output from it is A1=[L1,H1].

Then A1 is passed over again by HPF and LPF by adding a filter to each board. The performance of [L2 and H2] is A2=. Now A2 is sampled down by 2 to cause a compressed image.

Through taking the actions above we can do more than one degree [7].

Decompression Process

By actually taking a higher half matrix rectangle, extracting LPF and HPF images from compressed images are an LPF image and half a rectangle down is an HPF image.

Then the two images are summed up by 2. Actually, the sum of both images is taken into 1 image named B1.

Then, by vertically splitting [8], separate the LPF picture and the HPF image. The two halves obtained are filtered through LPF and HPF, providing the reconstructed image on each block of 32×32 blocks, summing up these halves.

In Tab. 1 illustrated the advantages and disadvantages of DCT and DWT.

This section shows some previous researcher’s studies for image compression using the DCT method.

Douak et al. [9] combined DCT transform with an adaptive block scanning to compress the color image in some steps. The first step is done by conversing from RGB color space to YCbCr color space and then using the DCT transform.

Using the Bisection technique, an iterative phase involving thresholding and quantization process was done to compress an image. Reconstructing the original image was done in the reverse process. The results explain that the system was efficient and was compared to a block truncation-based coder. The proposed method’s (M-3) PSNR higher than that of JPEG by approximately 2.22 db.

Khalil [10], suggested an entropy coder for the encoding of images based on the Discrete Cosine transformation. The proposed idea is to remove the consecutive non-zero coefficients in each 8×8 block which precede the zero coefficients. In the run-length decoder, the output is the number of coefficients of none-zero and then the coefficients themselves for each block. The method of decompression should be developed frequently. The findings of the simulation reveal that the new technique has a higher compression ratio than the previous form of entropy coding called the run process.

Chen et al. [11], proposed a modern paradigm incorporating the classical local DCT used in image compression algorithms with a regional noiseless calculation which is solved using second-order cone programming. The first step in the algorithm is to transfer DCT knowledge that is appropriate for reproducing poor-resolution image quality deterioration relative to survival, but the constraint is poor image quality.

Pandit et al. [12] proposed an efficient image compression approach depended on JPEG image compression. The objective of this research is to find the relation between image quality and compression mount. The results show that for images of size between 3 to 5 MB, the best results are got at quantization factor ranging from 25% to 30% where 80% to 85% compression can be found.

Pandey et al. [13] proposed the image compression techniques using the DCT method with different blocks and quantization methods for reducing the blocking artifacts in reconstruction images. The proposed method is subdivided into blocks (4×4, 8×8, 16×16, and 32×32) and the maximum image dimension is divided into a maximum block size & compressed the image. The performance between the original and reconstructed images is analyzed using several images and calculated with various quantization matrices with a PSNR score. From the results, it is found that when the maximum value is given by the quantization matrix then the restored image is distorted as its MSE value decreases.

Abd-Elhafiez et al. [14], the proposed compression technique is performed by RGB to YCbCr color transformation process. Second, canny edge detection is used to distinguish blocks into the edge and non-edge blocks. Using adaptive arithmetic coding, each Y, Cb, and Cr color variable is compressed, quantized, and coded by (DCT) method.

This section presents the previous researcher’s studies for image compression using DWT and hybrid techniques.

Chowdhury et al. [15] proposed an image compression process with a pruning proposal depended on the basic DWT technique. The steps of the proposed compression algorithm are choosing a wavelet level, the hard threshold is applied to the detail coefficients, reconstruct the image. This algorithm supplied sufficient high compression ratios with good image quality.

Khan [16] presented several image segmentation methods, upon studying various image segmentation techniques, it is proposed that the easiest approach to solving the image segmentation problem is a hybrid method for image segmentation composed of two or more techniques.

Sathik et al. [17] proposed the hybrid image compression technique and this hybrid technique segments the image into foreground and background regions based on edges. Then the regions compressed with various quality levels. This hybrid method provides the compression ratio and gives a desired quality compressed image.

Benchikh et al. [18], a hybrid DCT–DWT image compression solution was proposed to optimize the effects of both the DCT and DWT techniques. They have shown that, where several coefficients used for image restoration are small, DWT yields a better PSNR in image compression than DCT.

Elharar et al. [19], presented a compression method developed for the specific features of the digitally registered integral image.

The compression algorithm is based on a hybrid methodology that applies a four-dimensional transformation that incorporates the discreet wavelet transformation with the discrete cosine transformation. an earlier compression method developed for MPEG integral image-based image.

Agrwal et al. [20], presented the compression technique Hybrid Integer Wavelet Transform (IWT) and Discrete Cosine Transform (DCT) to achieve better-decompressed image output relative to compression technique DWT+DCT.

The proposed IWT+DCT-based hybrid compression technique reduces fractional loss relative to DWT-based compression, meaning that the proposed technique improves the image quality of decompression image with a high compression ratio comparable to DWT-based, and hybrid DWT DCT-based data compression techniques.