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SPIHT algorithm is well known reference for image coding algorithms. In this paper we present its specific extension called SM SPIHT related to region of interest encoding (ROI) but extending this approach further, ending with individual weight of importance for each pixel in image using the form of saliency map (SM). With carefully designed saliency map the approach has proven to be effective. The properties of proposed algorithm are presented and discussed in detail and further possible directions are outlined.

This paper presents an approach for an unequal error protection of facial features of personal ID images coding. We consider unequal error protection (UEP) strategies for the efficient progressive transmission of embedded image codes over noisy channels. This new method is based on the progressive image compression embedded zerotree wavelet (EZW) algorithm and UEP technique with defined region of interest (ROI). In this case is ROI equal facial features within personal ID image. ROI technique is important in applications with different parts of importance. In ROI coding, a chosen ROI is encoded with higher quality than the background (BG). Unequal error protection of image is provided by different coding techniques and encoding LL band separately. In our proposed method, image is divided into two parts (ROI, BG) that consist of more important bytes (MIB) and less important bytes (LIB). The proposed unequal error protection of image transmission has shown to be more appropriate to low bit rate applications, producing better quality output for ROI of the compresses image. The experimental results verify effectiveness of the design. The results of our method demonstrate the comparison of the UEP of image transmission with defined ROI with facial features and the equal error protection (EEP) over additive white gaussian noise (AWGN) channel.

This paper describes a new method of unequal error protection (UEP) for region of interest (ROI) with embedded zerotree wavelet algorithm (EZW). ROI technique is important in applications with different parts of importance. In ROI coding, a chosen ROI is encoded with higher quality than the background (BG). Unequal error protection of image is provided by different coding techniques. In our proposed method, image is divided into two parts (ROI, BG) that consist of more important bytes (MIB) and less important bytes (LIB). The experimental results verify effectiveness of the design. The results of our method demonstrate the comparison of the unequal error protection (UEP) of image transmission with defined ROI and the equal error protection (EEP) over multiple noisy channels., {"references":["J. M. Shapiro, \"Embedded image coding using zerotrees of wavelet\ncoefficients,\" IEEE Transactions on Signal Processing, vol. 41, no. 12,\npp. 3445-3462, 1993.","V. S. Shingate, T. R. 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The submitted paper deals with separate scanning of individual image segments. A new image processing approach based on image segmentation and segment scanning is presented. The resulting individual segments 1-dimensional representation provides higher neighbor pixel similarity than the 1-dimensional representation of the original image. This increased adjacent pixel similarity was achieved even without application of different recursive 2-dimensional scanning methods [4], such as Peano-Hilbert scanning method [1]. The resulting 1-dimensional image representation provides a good base for applying lossless compression methods, such as the entropic coding. The paper contains also results analysis of the traditional method scanned segment pixels and adjacent pixel differences from the entropy point of view. As these results indicate the lossy compression methods could be applicable using this approach as well and might improve the final results as confirmed by simple prediction algorithm results presented in this paper. More complex and sophisticated lossy compression algorithms application will be a part of the future work.

This paper introduces a method for restoration of missing arbitrarily shaped image segments using combined dircrete orthogonal trunsforms (DOT). The missing segment texture is successively interpoIate8/extrapolafed using the texture of the correctly decoded segments belonging to the same object, rhus having the same texture pattern.

This paper presents a new scheme for lossless image coding with SCAN image data scanning (Maniccam et al. (2001)). A methodology for creation of a convenient space-filling curve based on correlation is introduced.