JPEG DCT Quantization FDCT of 8x8 blocks. –Order in increasing spatial frequency (zigzag) Low frequencies have more shape information, get finer quantization. High’s often very small so go to zero after quantizing –If source has 8-bit entries ( s in [-2 7, ), can show that quantized DCT needs at most 11 bits (c in [-2 10, ])

JPEG DCT Quantization –Q(u,v) 8x8 table of integers [1..255] –F Q (u,v) = Round(F(u,v)/Q(u,v)) –Note can have one quantizer table for each image component. If not monochrome image, typically have usual one luminance, 2 chromatic channels. –Quantization tables can be in file or reference to standard –Standard quantizer based on JND. –See Wallace p 12.

JPEG DCT Intermediate Entropy Coding –Variable length code (Huffman): High occurrence symbols coded with fewer bits –Intermediate code: symbol pairs –symbol-1 chosen from table of symbols s i,j i is run length of zeros preceding quantized dct amplitude, j is length of huffman coding of the dct amplitude –i = 0…15, j= 1…10, and s 0,0 =‘EOB’ s 15,0 = ‘ZRL’ –symbol-2: Huffman encoding of dct amplitude –Finally, these 162 symbols are Huffman encoded.

JPEG components Y = 0.299R G B Cb = R G + 0.5B Cr = 0.5R G B Optionally subsample Cb, Cr – replace each pixel pair with its average. Not much loss of fidelity. Reduce data by 1/2*1/3+1/2*1/3 = 1/3 More shape info in achromatic than chromatic components. (Color vision poor at localization).

JPEG goodies Progressive mode - multiple scans, e.g. increasing spatial frequency so decoding gives shapes then detail Hierarchical encoding - multiple resolutions Lossless coding mode JFIF: –User embedded data –more than 3 components possible?

01 00 s1 01 s2 11 s3 100 s s s Traverse from root to leaf, then repeat: s3 s5 s3 s2 s4 Huffman Encoding

MPEG MPEG is to temporal compression as JPEG is to static compression: –utilizes known temporal psychophysics, both visual and audio –utilizes temporal redundancy for inter-frame coding (most of a picture doesn’t change very fast)

MPEG Data Organization Inter-frame differences within small blocks: –code difference; good if not much motion –code motion vector; good if translation Three kinds of frames: –I (Intra); “still” or reference frame, e.g JPEG –P (Predictive) coded relative to I or previous P –B (Bidirectional) coded relative to both previous and next I or P

MPEG Data Organization Goals of inter-frame coding: –high bit rate –random access Costs –memory –but memory is now cheap, hence HDTV arriving

Color TV Multiple standards - US, 2 in Europe, HDTV standards, Digital HDTV, Japanese analog.Digital HDTV, US: 525 lines (US HDTV is digital, and data stream defines resolution. Typically MPEG encoded to provide 1088 lines of which 1080 are displayed)

NTSC Analog Color TV 525 lines/frame Interlaced to reduce bandwidth –small interframe changes help Primary chromaticities:

NTSC Analog Color TV These yield RGB2XYZ = Y=0.299R G B (same as luminance channel for JPEG!) = Y value of white point. Cr = R-Y, Cb = B-Y with chromaticity: Cr: x=1.070, y=0; Cb: x=0.131 y=0; y(C)=0 => Y(C)=0 => achromatic

NTSC Analog Color TV Signals are gamma corrected under assumption of dim surround viewing conditions (high saturation). Y, Cr, Cb signals (E Y, E r, E b ) are sent per scan line; NTSC, SECAM, PAL do this in differing clever ways E Y typically with twice the bandwidth of E r, E b

NTSC Analog Color TV Y, Cr, Cb signals (E Y, E r, E b ) are sent per scan line; NTSC, SECAM, PAL do this in differing clever ways. – E Y with 4-10 x bandwidth of E r, E b –“Blue saving”

Digital HDTV FCC seeks proposals for advanced tv –Broadcast industry wants analog, 2x lines of NTSC for compatibility –Computer industry wanta digital 1993 (February) DHDTV demonstrated –in four incompatible systems 1993 (May) Grand Alliance formed

Digital HDTV 1996 (Dec 26) FCC accepts Grand Alliance Proposal of the Advanced Televisions Systems Committee ATSCAdvanced Televisions Systems Committee 1999 first DHDTV broadcasts

Digital HDTV lineshpixaspect framesframe rate ratio /9 progressive 24, 30 or /9 interlaced /9 progressive 24, 30 MPEG video compression Dolby AC-3 audio compression

Some gamuts SWOP ENCAD GA ink

Color naming A Computational model of Color Perception and Color Naming, Johann Lammens, Buffalo CS Ph.D. dissertation /diss/diss.html /diss/diss.html Cross language study of Berlin and Kay, 1969 “Basic colors”

Color naming “Basic colors” –Meaning not predicted from parts (e.g. blue, yellow, but not bluish) –not subsumed in another color category, (e.g. red but not crimson or scarlet) –can apply to any object (e.g. brown but not blond) –highly meaningful across informants (red but not chartruese)

Color naming “Basic colors” –Vary with language

Color naming Berlin and Kay experiment: –Elicit all basic color terms from 329 Munsell chips (40 equally spaced hues x 8 values plus 9 neutral hues –Find best representative –Find boundaries of that term

Color naming Berlin and Kay experiment: –Representative (“focus” constant across lang’s) Boundaries vary even across subjects and trials Lammens fits a linear+sigmoid model to each of R-B B-Y and Brightness data from macaque monkey LGN data of DeValois et. al.(1966) to get a color model. As usual this is two chromatic and one achromatic

Color naming To account for boundaries Lammens used standard statistical pattern recognition with the feature set determined by the coordinates in his color space defined by macaque LGN opponent responses. Has some theoretical but no(?) experimental justification for the model.