In the article a method and proper algorithms of reducing the size of compressed images and accelerating decoding in a modified JPEG format by using alternative difference color models with integer and half integer coefficients instead of the color model YCbCr are suggested. The use of the proposed difference color models reduces the size of each individual images due to intercomponent decorrelation and accelerates the decoding by using operations with integers and bitwise operations instead of operations with float numbers or scalable integers. Main findings of the study:
1. There is no universal color model that could allow to reach optimal decorrelation between components for all the types of images. Even for images of the same type, different color models may be optimal (in the sense of decorrelation).
2. In the graphic formats it is expedient to use discrete color models with integer or halfinteger coefficients for compression of individual images, instead of color models with valid coefficients, if they predict the decrease of compression coefficient. On average, such color models accelerate decoding by 3%.
3. For the image compression without losses, it is worthwhile to use difference color models with integer coefficients, and for the image compression with losses, it is better to use discrete color models with half-integer coefficients.
4. While compressing images with loses, color models with half-integer do not increase the range of possible values of individual components and do not significantly affect the image quality. If you want to minimize RMSE, then carrying component in a difference color model should be formed from the half sum of other components. If it is necessary to accelerate decoding as much as possible, then it is expedient to form the carrying component in a difference color model from one of the input components.
1. Salomon, D. (2006). Compression of data, images and sound. Moscow: Technosphere.
2. Miano, J. (2003). Format and image compression algorithms in action (p. 249-318). Moscow: Triumph.
3. Wallace, G.K. (1991). The JPEG still picture compression standard. Communication of ACM, 34(4), 30-44. https://doi.org/10.1145/103085.103089
4. Gonzalez, R. & Woods, R. (2005). Digital image processing. Moscow: Technosphere.
5. Shportko, A.V. (2010). Increase of the efficiency of color image compressions in the PNG format (Dis. PhD). Rivne State Humanitarian University, Rivne.
6. Vatolin, D., Ratushnyak, A., Smirnov, M. & Yukin, V. (2003). Data compression methods. Device archivers, image and video compression. Moscow: Dialogue-MIPHI.
7. Ponomarenko, S. (2002). Pixel and vector: principles of digital graphics. Chapter 17. Digital models. Digital RGB model. Retrieved from http://www.computerbooks.ru/books/Graphics/Book-Digital-Graphics/Glava%2....
8. Pratt, W. (1982). Digital image processing. Moscow: Mir.
9. Shportko, A.V. (2009). Usage of the difference color models for the compression of RGB images without losses. Selection and processing of information, 31 (107), 90-97.
10. Shportko, A.V. & Shportko, V.A. (2018). Application of difference color models for image compression in the modified JPEG graphic format (pp. 144-146), Computational methods and systems conversion information. V Sciences. and tech. conf. Lviv: FMI.
11. Shportko, A.V. (2018). The reduction of the size of the compressed interlaced scans of the JPEG graphic data format (pp. 298-299), The modern problems of mathematical modeling, computational methods and information technology. Intern. sciences. conf. Rivne: A.V Chervìnko.