A color space plays an important role in color image processing and color vision applications. While compressing images/videos, properties of the human visual system are used to remove image details unperceivable by the human eye, appropriately called psychovisual redundancies. This is where the effect of the color spaces' properties on compression efficiency is introduced. In this work, we study the suitability of various color spaces for compression of images and videos. This review work is undertaken in two stages. Initially, a comprehensive review of the published color spaces is done. These color spaces are classified and their advantages, limitations, and applications are also highlighted. Next, the color spaces are quantitatively analyzed and benchmarked in the perspective of image and video compression algorithms, to identify and evaluate crucial color space parameters for image and video compression algorithms.

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It is well-known that non-separable transforms can efficiently decorrelate arbitrarily directed textures that are often present in image and video content. Due to the computational complexity involved, it is usually applied as a secondary transform operating on low frequency primary transform coefficients. In order to represent a variety of arbitrary directional textures in natural images / videos, it is ideal to have sufficient coverage of secondary transform kernels for the codec to choose from. However, this may lead to increased signaling cost and encoder complexity. This paper proposes a context-adaptive secondary transform (CAST) kernel selection approach to enable the usage of more secondary transform kernels with no signaling cost increase and minimal encoder and decoder complexity increase. The proposed approach uses the variance of the top row and left column of reconstructed pixels adjacent to the transform block, if available, as a context for selecting the set of transform kernels. Experimental results show that, compared to libaom, the proposed algorithm achieves a luma BD-rate reduction of 2.17% and 3.11% for All Intra coding using PSNR and SSIM quality metrics, respectively.

This paper proposes a family of color image segmentation algorithms using genetic approach and color similarity threshold in terns of Just noticeable difference. Instead of segmenting and then optimizing, the proposed technique directly uses GA for optimized segmentation of color images. Application of GA on larger size color images is computationally heavy so they are applied on 4D-color image histogram table. The performance of the proposed algorithms is benchmarked on BSD dataset with color histogram based segmentation and Fuzzy C-means Algorithm using Probabilistic Rand Index (PRI). The proposed algorithms yield better analytical and visual results.