Data compression is one of the core fields of study for applications of image and video processing. The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form. In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image. These techniques are followed by entropy encoding such as Huffman coding to give additional compression. Huffman coding is optimal prefix code because of its implementation is more simple, faster, and easier than other codes. It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). We applied a hybrid algorithm on (DWT–DCT

For image compression, transform-based compression demonstrates greater robustness than spatial domain-based compression. The compression technique may be either lossless or loose. The compression ratio of data loss, however, is very high, but it suffers from lossy compression. Yet this is a high compression ratio, really.

In the lossless compression method, the decompressed image is identical to the original one, but with the compression ratio [

DCT cosine transforms the image from the space domain to the frequency domain, where the top left corner of the DCT matrix coefficient is the low-frequency component, and the frequency from the top left corner is decreased diagonally [

Discrete wavelet converts the image into four separate frequency bands called LL, LH, HL, and HH, where image characteristics are reflected by LL sub-bands and signal noise is shown by HH sub-bands [

The DCT algorithm shows better energy compaction features and requires fewer computational difficulties. This removes the blocking artifact and the misleading contouring results, though DWT, it is a multi-resolution compression process but DWT’s feature of energy compaction is less and induces a ringing effect.

In this paper, the transformation techniques chosen for image compression include discrete cosine transformation (DCT) and discrete wavelet transformation (DWT). The DCT transform is used in image coding (JPEG).

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

For

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

The 1D array is extracted and transmitted to the receiver from this the encoding process is completed [

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.

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

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

It has two filters: A high and a low pass [

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

Passing image through LPF and HPF and the Output from it is

Then A1 is passed over again by HPF and LPF by adding a filter to each board. The performance of [L2 and H2] is

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

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 [

In

DCT | DWT | |
---|---|---|

Advantages | Packing the most important information in little space of coefficients.It lessens the blocking artifact effect. | Permitting image multiresolution representation.Permitting progressive transmission |

Disadvantages | Blocking artifacts: Is a distortion that appears as an abnormally large pixel block, due to heavy compression. | Memory intensive. |

False contouring: Occurs when the graded area of the image is smoothly distorted bya deviation that looks like a contour map for specific images with gradually shaded areas. | Time-consuming. |

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

Douak et al. [

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 [

Chen et al. [

Pandit et al. [

Pandey et al. [

Abd-Elhafiez et al. [

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

Chowdhury et al. [

Khan [

Sathik et al. [

Benchikh et al. [

Elharar et al. [

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. [

The proposed

Hybrid of (

The various sizes (

In the Compression process, the input image is first converted from RGB to YCBCR, when this whole image is broken into blocks of

In the decompression process, all the reversed processes are performed in the decompression method. We decipher the quantized DCT coefficients and measure each block’s IDCT. Block is then quantized.

This section assesses the efficiency of various compression techniques for images. The hybrid techniques are applied to some color images. The results of (DCT, DWT) techniques, hybrid technique (DWT–DCT).

The proposed techniques were implemented using MATLAB and the evaluation parameters are CR, PSNR, and MSE.

Technique | CR | MSE | PSNR |
---|---|---|---|

DCT | 19.555 | 10.718 | 37.829 |

DWT | 12.195 | 16.85 | 34.669 |

(DWT–DCT |
2.5270 | 10.3068 | 37.9996 |

(DWT–DCT |
2.4854 | 10.1201 | 38.0790 |

(DWT–DCT |
2.5340 | 10.1872 | 38.0503 |

(DWT–DCT |
2.6233 | 10.2542 | 38.0218 |

(DWT–DCT |
2.7007 | 10.2303 | 38.0319 |

From

In this paper, the image compression techniques were analyzed by using the objective (PSNR, CR, and MSE) and subjective evaluation factors. Centered on the effects of the analysis set out above. For images, the suggested hybrid DWT–DCT algorithm can be shown to outperform the DCT and Daubechies-based DWT techniques. This is found that the restored images tend to be the strongest in the case of the DWT method, but with the DCT they are distorted by errors and incorrect contouring results.

The hybrid algorithm proposed has consistently higher PSNR and better quality for reconstruction.

It may also raise the impact of inaccurate contouring and images artifacts.